library(tidyverse)
library(plotly)
library(sf)
library(mapview)
library(tigris)
library(censusapi)
library(leaflet)
library(lehdr)


options(
  tigris_class = "sf",
  tigris_use_cache = TRUE
)

Sys.setenv(CENSUS_KEY="10dcd73d7c043e91bac9fb8d3989cbff54b08790")

Load social distancing data and blockgroups

Load the Safegraph social distancing data and San Jose blockgroups

# get SJ blockgroups 
# get San Jose block groups
scc_blockgroups <- block_groups("CA","Santa Clara", cb=F, progress_bar=F)

# Use tracts sent to us by San Jose
sj_tracts <- st_read("/Users/simonespeizer/pCloud Drive/Shared/SFBI/Data Library/San_Jose/CSJ_Census_Tracts/CSJ_Census_Tracts.shp") %>%
  st_as_sf() %>%
  st_transform(st_crs(scc_blockgroups))
## Reading layer `CSJ_Census_Tracts' from data source `/Users/simonespeizer/pCloud Drive/Shared/SFBI/Data Library/San_Jose/CSJ_Census_Tracts/CSJ_Census_Tracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 219 features and 9 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 6112856 ymin: 1869687 xmax: 6255982 ymax: 1996555
## epsg (SRID):    2227
## proj4string:    +proj=lcc +lat_1=38.43333333333333 +lat_2=37.06666666666667 +lat_0=36.5 +lon_0=-120.5 +x_0=2000000.0001016 +y_0=500000.0001016001 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=us-ft +no_defs
sj_citycouncil_disticts <- st_read("/Users/simonespeizer/pCloud Drive/Shared/SFBI/Data Library/San_Jose/City Council Districts/CITY_COUNCIL_DISTRICTS.shp") %>%
  st_as_sf() %>%
  st_transform(st_crs(scc_blockgroups))
## Reading layer `CITY_COUNCIL_DISTRICTS' from data source `/Users/simonespeizer/pCloud Drive/Shared/SFBI/Data Library/San_Jose/City Council Districts/CITY_COUNCIL_DISTRICTS.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 7 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 6112856 ymin: 1869687 xmax: 6255982 ymax: 1996555
## epsg (SRID):    2227
## proj4string:    +proj=lcc +lat_1=38.43333333333333 +lat_2=37.06666666666667 +lat_0=36.5 +lon_0=-120.5 +x_0=2000000.0001016 +y_0=500000.0001016001 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=us-ft +no_defs
# from code written by others to get SJ blockgroups
sj_blockgroups <-
  scc_blockgroups %>%
  st_centroid() %>%
  st_join(sj_tracts, left = F) %>%
  st_join(sj_citycouncil_disticts%>% dplyr::select(DISTRICTS)) %>%
  mutate(
    DISTRICTS = DISTRICTS %>% factor(levels = c("1","2","3","4","5","6","7","8","9","10"))
  ) %>%
  st_set_geometry(NULL) %>%
  left_join(scc_blockgroups%>% dplyr::select(GEOID), by = "GEOID") %>%
  st_as_sf() %>%
  dplyr::select(GEOID, DISTRICTS)

# the spatial join leaves off two blockgroups which are touching district 9. The following code assigns those to district 9
sj_blockgroups$DISTRICTS[is.na(sj_blockgroups$DISTRICTS)] <- 9

# code from others in the class to get social distancing data 
sj_socialdistancing <- readRDS("/Users/simonespeizer/pCloud Drive/Shared/SFBI/Restricted Data Library/Safegraph/covid19analysis/sj_socialdistancing.rds") %>% 
  mutate(date = date_range_start %>%  substr(1,10) %>% as.Date()) %>% 
  left_join(sj_blockgroups, by = c("origin_census_block_group" = "GEOID")) %>% 
  filter(!is.na(DISTRICTS))

# obtaining weekends
weekends <-
  sj_socialdistancing %>% 
  filter(!duplicated(date)) %>% 
  arrange(date) %>% 
  mutate(
    weekend = 
      ifelse(
        (date %>% as.numeric()) %% 7 %in% c(2,3),
        T,
        F
      )
  ) %>% 
  dplyr::select(date,weekend)

sj_socialdistancing <- 
  sj_socialdistancing %>% 
  left_join(weekends)

# date of the shelter in place order
shelter_start <- "2020-03-16" %>% as.Date()

# get average staying at home on weekdays in January and February
sj_pre_sd_at_home_average <- sj_socialdistancing %>% 
  filter(weekend == F) %>% 
  filter(date <  as.Date("2020-03-01")) %>%
  group_by(origin_census_block_group) %>% 
  summarize(completely_home_device_count = sum(completely_home_device_count), device_count = sum(device_count)) %>% 
  mutate(`% Completely at Home Pre Shelter` = (completely_home_device_count/device_count*100) %>% round(1), `% not completely at home pre shelter` = (100 - `% Completely at Home Pre Shelter`))

Obtaining demographic variables

Here I obtain various demographic data, including income (percent below 50% and 80% of area median income), vehicle ownership, age, English language ability, and occupants per room.

# obtain the saved census data 
setwd("~/Documents/2020 Spring Quarter/CEE 218Z")
acs_vars = readRDS("censusData2018_acs_acs5.rds")
setwd("~/Documents/2020 Spring Quarter/CEE 218Z/covid19")
# load in income data - code adapted from other students
sj_median_income_by_block <-
  getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "B19013_001E"
  ) %>%
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  rename(
    Median_Income = B19013_001E 
  ) %>% 
  filter(!is.na(Median_Income)) %>% 
  left_join(sj_blockgroups, by = c("blockgroup" = "GEOID")) %>% #this code gives each blockgroup a district designation
  filter(
    !is.na(DISTRICTS)
  ) %>% 
  
  # this code joins our census data with the social distancing data, processed as shown below
  left_join(sj_socialdistancing %>%  
                          filter(weekend == F) %>% 
                          filter(date > shelter_start) %>%
                          group_by(origin_census_block_group) %>% 
                          summarize(
                                    completely_home_device_count = sum(completely_home_device_count),
                                    device_count = sum(device_count)) %>% 
                          mutate(`% Completely at Home` = (completely_home_device_count/device_count*100) %>% round(1), 
                                 `% not completely at home` = (100 - `% Completely at Home`)),
            by = c("blockgroup" = "origin_census_block_group")
  ) %>% 
  filter(
    !is.na(device_count)
  ) %>% 
  left_join(sj_pre_sd_at_home_average %>% dplyr::select(origin_census_block_group, `% Completely at Home Pre Shelter`, `% not completely at home pre shelter`), by = c("blockgroup" = "origin_census_block_group"))

sj_ami_by_block <-
  getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B19001)"
  ) %>%
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>%
  group_by(blockgroup) %>% 
  summarize(
    Total = B19001_001E,
    `Under 75,000` = sum(B19001_002E, B19001_003E, B19001_004E, B19001_005E, B19001_006E, B19001_007E, B19001_008E, B19001_009E, B19001_010E, B19001_011E, B19001_012E),
    #sum(lapply(2:12, function(x) as.name(paste0("B19001_00",x,"E"))))
    `Under 100,000` = sum(B19001_002E, B19001_003E, B19001_004E, B19001_005E, B19001_006E, B19001_007E, B19001_008E, B19001_009E, B19001_010E, B19001_011E, B19001_012E, B19001_013E), 
    `Under 125,000` = sum(B19001_002E, B19001_003E, B19001_004E, B19001_005E, B19001_006E, B19001_007E, B19001_008E, B19001_009E, B19001_010E, B19001_011E, B19001_012E, B19001_013E, B19001_014E)
  ) %>% 
  mutate(
    `% under 75,000` = `Under 75,000` / Total * 100,
    `% over 75,000` = (100 - `% under 75,000`),
    `% under 100,000` = `Under 100,000` / Total * 100,
    `% over 100,000` = (100 - `% under 100,000`),
    `% under 125,000` = `Under 125,000` / Total * 100,
    `% over 125,000` = (100 - `% under 125,000`),
  ) %>% 
  left_join(sj_median_income_by_block %>% dplyr::select(-Median_Income)
  ) %>% 
  filter(!is.na(device_count))
# loading in language data - code adapted from other students
sj_lang_by_block <-
  getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B16004)"
  )  %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(
    key = "variable",
    value = "estimate", 
    - blockgroup
  ) %>% 
  left_join(acs_vars, by = c("variable" = "name")) %>% 
  mutate(
    tier = substr(label,lapply(label, function(x) max(unlist(gregexpr('!!',x)))+2),nchar(label))
  ) %>% 
  filter(tier %in% c('Speak English "not well"', 
                     'Speak English "not at all"', 
                     'Total', 'Speak Spanish', 
                     'Speak Asian and Pacific Island languages')) %>% 
  group_by(blockgroup, tier) %>% 
  summarise(
    estimate1 = sum(estimate)
  ) %>% 
  spread(
    key = "tier",
    value = "estimate1"
  ) %>% 
  mutate(
    `% speaking english < well` = (`Speak English "not well"` + `Speak English "not at all"`) / Total * 100,
    `% speaking english > well` = (100 - `% speaking english < well`),
    `% speaking spanish` = (`Speak Spanish`/ Total) * 100,
    `% not speaking spanish` = (100 - `% speaking spanish`),
    `% speaking api` = (`Speak Asian and Pacific Island languages` / Total) * 100
  ) %>% 
  left_join(sj_median_income_by_block %>% dplyr::select(-Median_Income)) %>% 
  filter(!is.na(device_count)) %>% 
  mutate(log_perc = log(`% speaking english < well`))
# loading in age data - specifically looking at percentage 65+ and percentage <30
sj_age_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B01001)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(
    key = "variable",
    value = "estimate", 
    - blockgroup
  ) %>% 
  mutate(
    label = acs_vars$label[match(variable,acs_vars$name)]
  ) %>% 
  dplyr::select(-variable) %>% 
  separate(
    label,
    into = c(NA,NA,"sex","age"),
    sep = "!!"
  ) %>% filter(!is.na(age)) %>% 
  mutate(elderly = ifelse(age %in% c("65 and 66 years", "67 to 69 years", "70 to 74 years", "75 to 79 years", "80 to 84 years", "85 years and over"), estimate, NA), `less than 30` = ifelse(age %in% c("Under 5 years", "5 to 9 years", "10 to 14 years", "15 to 17 years", "18 and 19 years", "20 years", "21 years", "22 to 24 years", "25 to 29 years"), estimate, NA), `less than 18` = ifelse(age %in% c("Under 5 years", "5 to 9 years", "10 to 14 years", "15 to 17 years"), estimate, NA), `20-29` = ifelse(age %in% c("20 years", "21 years", "22 to 24 years", "25 to 29 years"), estimate, NA)) %>% 
  group_by(blockgroup) %>% 
  summarize(elderly = sum(elderly, na.rm = T), `less than 30` = sum(`less than 30`, na.rm = T), total = sum(estimate, na.rm = T), `less than 18` = sum(`less than 18`, na.rm = T), `20-29` = sum(`20-29`, na.rm = T)) %>% 
  mutate(`percent elderly` = elderly*100 / total, `percent less than 30` = `less than 30`*100 / total, `percent nonelderly` = (100 - `percent elderly`), `percent less than 18` = `less than 18`*100/total, `percent 20-29` = `20-29`*100/total) %>% 
  left_join(sj_median_income_by_block %>% dplyr::select(-Median_Income)) %>% 
  filter(!is.na(device_count))

# keep all age categories separated
sj_all_age_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B01001)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(
    key = "variable",
    value = "estimate", 
    - blockgroup
  ) %>% 
  mutate(
    label = acs_vars$label[match(variable,acs_vars$name)]
  ) %>% 
  dplyr::select(-variable) %>% 
  separate(
    label,
    into = c(NA,NA,"sex","age"),
    sep = "!!"
  ) %>% filter(!is.na(age)) %>% 
  group_by(blockgroup, age) %>%
  summarize(total_by_age = sum(estimate)) %>%
  spread(key = age, value = total_by_age) %>%
  left_join(sj_age_by_block %>% dplyr::select(blockgroup, total)) %>% 
  left_join(sj_median_income_by_block %>% dplyr::select(device_count, blockgroup)) %>% 
  filter(!is.na(device_count)) %>%
  dplyr::select(-device_count)
# get data on vehicles available as vehicles allocation
sj_vehicles_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B992512)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  dplyr::select(B992512_001E, blockgroup) %>%
  rename(total_vehicles = B992512_001E, blockgroup = blockgroup) %>%
  left_join(sj_age_by_block %>% dplyr::select_if(!names(.) %in% c("elderly", "percent elderly", "less than 30", "percent less than 30"))) %>%
  mutate(`vehicles per capita` = total_vehicles / total) %>%
  filter(!is.na(device_count)) 

# also get data on vehicles available as households without a vehicle
sj_no_vehicles_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B25044)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(key = "variable", value = "estimate", -blockgroup) %>% 
  mutate(label = acs_vars$label[match(variable,acs_vars$name)]) %>% 
  dplyr::select(-variable) %>%
  separate(label, into = c(NA, NA, NA,"vehicles"), sep = "!!") %>% 
  filter(!is.na(vehicles)) %>%
  group_by(blockgroup, vehicles) %>%
  summarize(grouped_vehicles = sum(estimate)) %>%
  spread(key = vehicles, value = grouped_vehicles) %>%
  mutate(total_nums = `1 vehicle available` + `2 vehicles available` + `3 vehicles available` + `4 vehicles available` + `5 or more vehicles available` + `No vehicle available`, `percent no vehicles` = `No vehicle available`*100 / total_nums, `percent with vehicles` = (100-`percent no vehicles`)) %>%
  left_join(sj_age_by_block %>% dplyr::select_if(!names(.) %in% c("elderly", "percent elderly", "less than 30", "percent less than 30"))) %>%
  filter(!is.na(device_count))
# get data on occupants per room
sj_occupants_per_room_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B25014)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(key = "variable", value = "estimate", -blockgroup) %>% 
  mutate(label = acs_vars$label[match(variable,acs_vars$name)]) %>% 
  dplyr::select(-variable) %>% 
  separate(label, into = c(NA, NA, NA,"occupants per room"), sep = "!!") %>% 
  filter(!is.na(`occupants per room`)) %>%
  group_by(blockgroup, `occupants per room`) %>%
  summarize(estimate_tot = sum(estimate)) %>% 
  spread(key = `occupants per room`, value = estimate_tot) %>%
  mutate(total_nums = `0.50 or less occupants per room` + `0.51 to 1.00 occupants per room` + `1.01 to 1.50 occupants per room` + `1.51 to 2.00 occupants per room` + `2.01 or more occupants per room`, `percent 1 or more` = (`1.01 to 1.50 occupants per room` + `1.51 to 2.00 occupants per room` + `2.01 or more occupants per room`) * 100/ total_nums, `percent less than 1` = (100-`percent 1 or more`)) %>%
  left_join(sj_age_by_block %>% dplyr::select_if(!names(.) %in% c("elderly", "percent elderly", "less than 30", "percent less than 30"))) %>%
  filter(!is.na(device_count))

Testing correlations

In the plots below, I show the selected variables against percent of devices completely at home since the shelter-in-place order started, as well as against percent of devices pre-shelter-in-place for comparison.

Age

# age
sj_age_by_block %>%
  ggplot(aes(
  x = `percent less than 30`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
labs(
    x = "Percent of residents younger than 30",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Young Age Groups"
  )

young_model <- lm(sj_age_by_block$`% not completely at home` ~ sj_age_by_block$`percent less than 30`)
summary(young_model)
## 
## Call:
## lm(formula = sj_age_by_block$`% not completely at home` ~ sj_age_by_block$`percent less than 30`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.305  -4.595  -0.326   4.013  39.401 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            44.89889    1.46159  30.719  < 2e-16 ***
## sj_age_by_block$`percent less than 30`  0.17542    0.03705   4.735 2.77e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.014 on 567 degrees of freedom
## Multiple R-squared:  0.03803,    Adjusted R-squared:  0.03634 
## F-statistic: 22.42 on 1 and 567 DF,  p-value: 2.775e-06
sj_age_by_block %>% filter(`percent elderly` < 50) %>% # get rid of extreme outliers
  ggplot(aes(
  x = `percent elderly`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents 65 and older",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Elderly Population"
  )

elderly_model <- lm(`% not completely at home` ~ `percent elderly`, sj_age_by_block %>% filter(`percent elderly` < 50))
summary(elderly_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent elderly`, 
##     data = sj_age_by_block %>% filter(`percent elderly` < 50))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.329  -4.899  -0.267   4.127  34.323 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       53.82116    0.75636  71.158  < 2e-16 ***
## `percent elderly` -0.17173    0.05223  -3.288  0.00107 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.077 on 564 degrees of freedom
## Multiple R-squared:  0.01881,    Adjusted R-squared:  0.01707 
## F-statistic: 10.81 on 1 and 564 DF,  p-value: 0.001071
sj_age_by_block %>% 
  ggplot(aes(
  x = `percent less than 18`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents less than 18",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Child Population"
  )

child_model <- lm(`% not completely at home` ~ `percent less than 18`, sj_age_by_block)
summary(child_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent less than 18`, 
##     data = sj_age_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.573  -5.033  -0.242   4.352  39.666 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            54.21192    1.18234  45.852   <2e-16 ***
## `percent less than 18` -0.11474    0.05038  -2.277   0.0231 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.134 on 567 degrees of freedom
## Multiple R-squared:  0.009064,   Adjusted R-squared:  0.007316 
## F-statistic: 5.186 on 1 and 567 DF,  p-value: 0.02314
sj_age_by_block %>% 
  ggplot(aes(
  x = `percent 20-29`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents ages 20-29",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Population Ages 20-29"
  )

young_adult_model <- lm(`% not completely at home` ~ `percent 20-29`, sj_age_by_block)
summary(young_adult_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent 20-29`, data = sj_age_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.315  -4.621  -0.302   4.467  39.461 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     48.23891    0.65596  73.539  < 2e-16 ***
## `percent 20-29`  0.24490    0.04081   6.002 3.49e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.923 on 567 degrees of freedom
## Multiple R-squared:  0.05973,    Adjusted R-squared:  0.05807 
## F-statistic: 36.02 on 1 and 567 DF,  p-value: 3.486e-09
# compare this to pre-shelter-in-place behavior
sj_age_by_block %>%
  ggplot(aes(
  x = `percent less than 30`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
labs(
    x = "Percent of residents younger than 30",
    y = "Percent devices leaving home pre-shelter-in-place",
    title = "San Jose: Staying at Home and Young Age Groups Pre Shelter-in-Place"
  )

young_model2 <- lm(sj_age_by_block$`% not completely at home pre shelter` ~ sj_age_by_block$`percent less than 30`)
summary(young_model2)
## 
## Call:
## lm(formula = sj_age_by_block$`% not completely at home pre shelter` ~ 
##     sj_age_by_block$`percent less than 30`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.1939  -2.8160  -0.1557   2.9950  16.7071 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            81.87032    0.82253   99.53  < 2e-16 ***
## sj_age_by_block$`percent less than 30` -0.11072    0.02085   -5.31 1.57e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.51 on 567 degrees of freedom
## Multiple R-squared:  0.04738,    Adjusted R-squared:  0.0457 
## F-statistic:  28.2 on 1 and 567 DF,  p-value: 1.573e-07
sj_age_by_block %>% filter(`percent elderly` < 50) %>% # get rid of extreme outliers
  ggplot(aes(
  x = `percent elderly`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents 65 and older",
    y = "Percent devices leaving home on weekdays pre-shelter-in-place",
    title = "San Jose: Staying at Home and Elderly Population Pre Shelter-in-Place"
  )

elderly_model2 <- lm(`% not completely at home pre shelter` ~ `percent elderly`, sj_age_by_block %>% filter(`percent elderly` < 50))
summary(elderly_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `percent elderly`, 
##     data = sj_age_by_block %>% filter(`percent elderly` < 50))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.236  -2.830  -0.158   3.145  14.296 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        75.9045     0.4257 178.295  < 2e-16 ***
## `percent elderly`   0.1329     0.0294   4.522 7.47e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.546 on 564 degrees of freedom
## Multiple R-squared:  0.03499,    Adjusted R-squared:  0.03328 
## F-statistic: 20.45 on 1 and 564 DF,  p-value: 7.466e-06
sj_age_by_block %>% 
  ggplot(aes(
  x = `percent less than 18`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents less than 18",
    y = "Percent devices leaving home on weekdays pre shelter-in-place",
    title = "San Jose: Social Distancing and Child Population Pre Shelter"
  )

child_model2 <- lm(`% not completely at home pre shelter` ~ `percent less than 18`, sj_age_by_block)
summary(child_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `percent less than 18`, 
##     data = sj_age_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -26.3901  -3.0050   0.0411   3.1602  12.4458 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            76.08044    0.66828 113.845   <2e-16 ***
## `percent less than 18`  0.06849    0.02848   2.405   0.0165 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.597 on 567 degrees of freedom
## Multiple R-squared:  0.0101, Adjusted R-squared:  0.008352 
## F-statistic: 5.784 on 1 and 567 DF,  p-value: 0.01649
sj_age_by_block %>% 
  ggplot(aes(
  x = `percent 20-29`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents ages 20-29",
    y = "Percent devices leaving home on weekdays pre shelter-in-place",
    title = "San Jose: Social Distancing and Population Ages 20-29 Pre Shelter"
  )

young_adult_model2 <- lm(`% not completely at home pre shelter` ~ `percent 20-29`, sj_age_by_block)
summary(young_adult_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `percent 20-29`, 
##     data = sj_age_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.0695  -2.7320  -0.1156   2.8283  15.9003 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     80.23612    0.36071 222.440  < 2e-16 ***
## `percent 20-29` -0.18877    0.02244  -8.413 3.26e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.357 on 567 degrees of freedom
## Multiple R-squared:  0.111,  Adjusted R-squared:  0.1094 
## F-statistic: 70.78 on 1 and 567 DF,  p-value: 3.264e-16

Income

# income - less than $75000
sj_ami_by_block %>% 
  ggplot(aes(
  x = `% over 75,000`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $75,000 (50% AMI) annually",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Households Above 50% AMI"
  )

income_75_model <- lm(`% not completely at home` ~ `% over 75,000`, sj_ami_by_block)
summary(income_75_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `% over 75,000`, data = sj_ami_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.068  -4.630  -0.633   4.135  32.635 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     64.27172    1.07390   59.85   <2e-16 ***
## `% over 75,000` -0.20381    0.01655  -12.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.169 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2113, Adjusted R-squared:  0.2099 
## F-statistic: 151.6 on 1 and 566 DF,  p-value: < 2.2e-16
# income - less than $100000
sj_ami_by_block %>% 
  ggplot(aes(
  x = `% over 100,000`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $100,000 (80% AMI) annually",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Households Above 80% AMI"
  )

income_100_model <- lm(`% not completely at home` ~ `% over 100,000`, sj_ami_by_block)
summary(income_100_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `% over 100,000`, data = sj_ami_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.5007  -4.7327  -0.3607   3.8991  31.0438 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      61.80826    0.83877   73.69   <2e-16 ***
## `% over 100,000` -0.20048    0.01537  -13.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.078 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.231,  Adjusted R-squared:  0.2297 
## F-statistic: 170.1 on 1 and 566 DF,  p-value: < 2.2e-16
# income - less than $125000
sj_ami_by_block %>% 
  ggplot(aes(
  x = `% over 125,000`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $125,000 annually",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Households Above $125,000"
  )

income_125_model <- lm(`% not completely at home` ~ `% over 125,000`, sj_ami_by_block)
summary(income_125_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `% over 125,000`, data = sj_ami_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.9783  -4.4021  -0.6648   3.8966  30.1129 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      60.09270    0.70606   85.11   <2e-16 ***
## `% over 125,000` -0.20695    0.01558  -13.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.048 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2376, Adjusted R-squared:  0.2362 
## F-statistic: 176.3 on 1 and 566 DF,  p-value: < 2.2e-16
# compare to pre shelter in place
sj_ami_by_block %>% 
  ggplot(aes(
  x = `% over 75,000`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $75,000 (50% AMI) annually",
    y = "Percent devices leaving home on weekdays pre-shelter-in-place",
    title = "San Jose: Staying at Home and Households Above 50% AMI Pre Shelter-in-Place"
  )

income_75_model2 <- lm(`% not completely at home pre shelter` ~ `% over 75,000`, sj_ami_by_block)
summary(income_75_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `% over 75,000`, 
##     data = sj_ami_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.4357  -2.7003  -0.1437   2.7764  16.6680 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     72.61447    0.65712 110.504  < 2e-16 ***
## `% over 75,000`  0.08029    0.01013   7.926 1.21e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.386 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.09991,    Adjusted R-squared:  0.09832 
## F-statistic: 62.83 on 1 and 566 DF,  p-value: 1.206e-14
# income - less than $100000
sj_ami_by_block %>% 
  ggplot(aes(
  x = `% over 100,000`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $100,000 (80% AMI) annually",
    y = "Percent devices leaving home on weekdays pre-shelter-in-place",
    title = "San Jose: Staying Home and Households Below 80% AMI Pre Shelter-in-Place"
  )

income_100_model2 <- lm(`% not completely at home pre shelter` ~ `% over 100,000`, sj_ami_by_block)
summary(income_100_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `% over 100,000`, 
##     data = sj_ami_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.5034  -2.6406   0.0803   2.5599  16.9387 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      73.26132    0.51177 143.152   <2e-16 ***
## `% over 100,000`  0.08532    0.00938   9.096   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.319 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1275, Adjusted R-squared:  0.126 
## F-statistic: 82.73 on 1 and 566 DF,  p-value: < 2.2e-16
# over 125000
sj_ami_by_block %>% 
  ggplot(aes(
  x = `% over 125,000`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $125,000 annually",
    y = "Percent devices leaving home on weekdays pre-shelter-in-place",
    title = "San Jose: Social Distancing and Households Below $125,000 Pre Shelter-in-Place"
  )

income_125_model2 <- lm(`% not completely at home pre shelter` ~ `% over 125,000`, sj_ami_by_block)
summary(income_125_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `% over 125,000`, 
##     data = sj_ami_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.353  -2.556   0.022   2.522  16.560 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      73.640242   0.425069   173.2   <2e-16 ***
## `% over 125,000`  0.096607   0.009382    10.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.243 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1578, Adjusted R-squared:  0.1563 
## F-statistic:   106 on 1 and 566 DF,  p-value: < 2.2e-16

Language

# language
sj_lang_by_block %>% 
  ggplot(aes(
  x = `% speaking english > well`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of individuals speaking English well",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and English Language Ability"
  )

english_ability_model <- lm(`% not completely at home` ~ `% speaking english > well`, sj_lang_by_block)
summary(english_ability_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `% speaking english > well`, 
##     data = sj_lang_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.368  -4.739  -0.403   3.890  37.997 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 66.80735    3.24864  20.565  < 2e-16 ***
## `% speaking english > well` -0.17105    0.03642  -4.696 3.33e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.017 on 567 degrees of freedom
## Multiple R-squared:  0.03744,    Adjusted R-squared:  0.03574 
## F-statistic: 22.05 on 1 and 567 DF,  p-value: 3.332e-06
sj_lang_by_block %>% 
  ggplot(aes(
  x = `% not speaking spanish`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of individuals not speaking Spanish",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Spanish Language Ability"
  )

spanish_speaking_model <- lm(`% not completely at home` ~ `% not speaking spanish`, sj_lang_by_block)
summary(spanish_speaking_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `% not speaking spanish`, 
##     data = sj_lang_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.273  -4.268  -0.575   3.458  37.202 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              63.87866    1.26890  50.342   <2e-16 ***
## `% not speaking spanish` -0.15750    0.01581  -9.964   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.538 on 567 degrees of freedom
## Multiple R-squared:  0.149,  Adjusted R-squared:  0.1475 
## F-statistic: 99.28 on 1 and 567 DF,  p-value: < 2.2e-16
# compare to pre shelter in place
sj_lang_by_block %>% 
  ggplot(aes(
  x = `% speaking english > well`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of individuals speaking English well",
    y = "Percent devices leaving home on weekdays pre-shelter-in-place",
    title = "San Jose: Staying at Home and English Language Ability Pre Shelter-in-Place"
  )

english_ability_model2 <- lm(`% not completely at home pre shelter` ~ `% speaking english > well`, sj_lang_by_block)
summary(english_ability_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `% speaking english > well`, 
##     data = sj_lang_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.9364  -2.4342   0.0388   3.0316  12.3011 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 60.84131    1.73337  35.100   <2e-16 ***
## `% speaking english > well`  0.18913    0.01943   9.732   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.277 on 567 degrees of freedom
## Multiple R-squared:  0.1431, Adjusted R-squared:  0.1416 
## F-statistic:  94.7 on 1 and 567 DF,  p-value: < 2.2e-16
sj_lang_by_block %>% 
  ggplot(aes(
  x = `% not speaking spanish`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of individuals not speaking Spanish",
    y = "Percent devices leaving home on weekdays pre shelter-in-place",
    title = "San Jose: Staying at Home and Spanish Language Ability Pre Shelter-in-Place"
  )

spanish_speaking_model2 <- lm(`% not completely at home pre shelter` ~ `% not speaking spanish`, sj_lang_by_block)
summary(spanish_speaking_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `% not speaking spanish`, 
##     data = sj_lang_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.793  -2.540  -0.002   2.680  11.988 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              71.228200   0.726831  97.998   <2e-16 ***
## `% not speaking spanish`  0.082206   0.009054   9.079   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.318 on 567 degrees of freedom
## Multiple R-squared:  0.1269, Adjusted R-squared:  0.1254 
## F-statistic: 82.43 on 1 and 567 DF,  p-value: < 2.2e-16

Occupants per room

# occupants per room
sj_occupants_per_room_by_block %>% 
  ggplot(aes(
  x = `percent less than 1`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of households with 1 or fewer occupant per room",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Room Occupancy"
  )

occupants_model <- lm(`% not completely at home` ~ `percent less than 1`, sj_occupants_per_room_by_block)
summary(occupants_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent less than 1`, 
##     data = sj_occupants_per_room_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.498  -4.774  -0.098   3.776  34.802 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           70.73449    2.84276  24.882  < 2e-16 ***
## `percent less than 1` -0.21236    0.03131  -6.783 2.97e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.762 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.07518,    Adjusted R-squared:  0.07355 
## F-statistic: 46.01 on 1 and 566 DF,  p-value: 2.969e-11
# compare to pre shelter in place
sj_occupants_per_room_by_block %>% 
  ggplot(aes(
  x = `percent less than 1`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of households with 1 or fewer occupant per room",
    y = "Percent devices leaving home on weekdays pre shelter-in-place",
    title = "San Jose: Staying at Home and Room Occupancy Pre Shelter-in-Place"
  )

occupants_model2 <- lm(`% not completely at home pre shelter` ~ `percent less than 1`, sj_occupants_per_room_by_block)
summary(occupants_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `percent less than 1`, 
##     data = sj_occupants_per_room_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.3246  -2.6506  -0.2808   2.7536  17.0509 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           62.88485    1.57437  39.943   <2e-16 ***
## `percent less than 1`  0.16329    0.01734   9.418   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.299 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1355, Adjusted R-squared:  0.134 
## F-statistic:  88.7 on 1 and 566 DF,  p-value: < 2.2e-16

Vehicle ownership

# vehicles
sj_vehicles_by_block %>% 
  ggplot(aes(
  x = `vehicles per capita`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Number of vehicles per capita",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Vehicles Per Capita"
  )

# vehicles - percent with no vehicles
sj_no_vehicles_by_block %>% 
  ggplot(aes(
  x = `percent with vehicles`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of housholds with vehicles available",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Vehicle Availability"
  )

vehicles_model <- lm(`% not completely at home` ~ `percent with vehicles`, sj_no_vehicles_by_block)
summary(vehicles_model)
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent with vehicles`, 
##     data = sj_no_vehicles_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.480  -4.684  -0.322   4.556  37.220 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             73.22880    4.98157  14.700  < 2e-16 ***
## `percent with vehicles` -0.22749    0.05223  -4.356 1.57e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.94 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03243,    Adjusted R-squared:  0.03072 
## F-statistic: 18.97 on 1 and 566 DF,  p-value: 1.574e-05
# compare to pre shelter in place
sj_no_vehicles_by_block %>% 
  ggplot(aes(
  x = `percent with vehicles`,
  y = `% not completely at home pre shelter`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of housholds with vehicles available",
    y = "Percent devices leaving home on weekdays pre shelter-in-place",
    title = "San Jose: Social Distancing and Vehicle Availability Pre Shelter-in-Place"
  )

vehicles_model2 <- lm(`% not completely at home pre shelter` ~ `percent with vehicles`, sj_no_vehicles_by_block)
summary(vehicles_model2)
## 
## Call:
## lm(formula = `% not completely at home pre shelter` ~ `percent with vehicles`, 
##     data = sj_no_vehicles_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.5618  -2.9606  -0.0694   3.0006  12.6053 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             63.25942    2.83717  22.297  < 2e-16 ***
## `percent with vehicles`  0.15084    0.02975   5.071 5.37e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.522 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.04346,    Adjusted R-squared:  0.04177 
## F-statistic: 25.72 on 1 and 566 DF,  p-value: 5.37e-07

Multiple regression analyses

Income, age, language, and occupants per room

# multiple regression 
modeltest <- lm(sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + sj_age_by_block$`percent less than 30` + sj_lang_by_block$`% speaking english > well` + sj_occupants_per_room_by_block$`percent less than 1`)
summary(modeltest)
## 
## Call:
## lm(formula = sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + 
##     sj_age_by_block$`percent less than 30` + sj_lang_by_block$`% speaking english > well` + 
##     sj_occupants_per_room_by_block$`percent less than 1`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.2655  -4.3384  -0.7492   3.6871  29.9600 
## 
## Coefficients:
##                                                       Estimate Std. Error
## (Intercept)                                          51.357400   4.574598
## sj_ami_by_block$`% over 125,000`                     -0.236228   0.020629
## sj_age_by_block$`percent less than 30`                0.007948   0.040347
## sj_lang_by_block$`% speaking english > well`          0.145889   0.044201
## sj_occupants_per_room_by_block$`percent less than 1` -0.036662   0.044226
##                                                      t value Pr(>|t|)    
## (Intercept)                                           11.227  < 2e-16 ***
## sj_ami_by_block$`% over 125,000`                     -11.451  < 2e-16 ***
## sj_age_by_block$`percent less than 30`                 0.197  0.84391    
## sj_lang_by_block$`% speaking english > well`           3.301  0.00103 ** 
## sj_occupants_per_room_by_block$`percent less than 1`  -0.829  0.40747    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.995 on 563 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.253,  Adjusted R-squared:  0.2477 
## F-statistic: 47.67 on 4 and 563 DF,  p-value: < 2.2e-16

Education and income

educ_income_model <- lm(sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + sj_education_by_block$`percent associates or higher`)
summary(educ_income_model)
## 
## Call:
## lm(formula = sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + 
##     sj_education_by_block$`percent associates or higher`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.6193  -4.4926  -0.8443   3.6379  31.3968 
## 
## Coefficients:
##                                                      Estimate Std. Error
## (Intercept)                                          61.64422    0.79199
## sj_ami_by_block$`% over 125,000`                     -0.14437    0.02163
## sj_education_by_block$`percent associates or higher` -0.08741    0.02126
##                                                      t value Pr(>|t|)    
## (Intercept)                                           77.835  < 2e-16 ***
## sj_ami_by_block$`% over 125,000`                      -6.675 5.92e-11 ***
## sj_education_by_block$`percent associates or higher`  -4.112 4.50e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.951 on 565 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2597, Adjusted R-squared:  0.2571 
## F-statistic: 99.11 on 2 and 565 DF,  p-value: < 2.2e-16

Internet and income

educ_income_model <- lm(sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + sj_internet_by_block$`percent high speed`)
summary(educ_income_model)
## 
## Call:
## lm(formula = sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + 
##     sj_internet_by_block$`percent high speed`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -22.1458  -4.4058  -0.5434   3.8081  29.8800 
## 
## Coefficients:
##                                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               64.83584    2.19898  29.485   <2e-16
## sj_ami_by_block$`% over 125,000`          -0.17672    0.02043  -8.649   <2e-16
## sj_internet_by_block$`percent high speed` -0.07421    0.03260  -2.277   0.0232
##                                              
## (Intercept)                               ***
## sj_ami_by_block$`% over 125,000`          ***
## sj_internet_by_block$`percent high speed` *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.022 on 565 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2445, Adjusted R-squared:  0.2418 
## F-statistic: 91.42 on 2 and 565 DF,  p-value: < 2.2e-16

Income and Spanish language ability

income_spanish_model <- lm(sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + sj_lang_by_block$`% not speaking spanish`)
summary(income_spanish_model)
## 
## Call:
## lm(formula = sj_ami_by_block$`% not completely at home` ~ sj_ami_by_block$`% over 125,000` + 
##     sj_lang_by_block$`% not speaking spanish`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.1271  -4.4548  -0.7803   3.5867  29.8008 
## 
## Coefficients:
##                                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               62.77452    1.18514  52.968  < 2e-16
## sj_ami_by_block$`% over 125,000`          -0.17229    0.01981  -8.698  < 2e-16
## sj_lang_by_block$`% not speaking spanish` -0.05281    0.01881  -2.808  0.00515
##                                              
## (Intercept)                               ***
## sj_ami_by_block$`% over 125,000`          ***
## sj_lang_by_block$`% not speaking spanish` ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.006 on 565 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.248,  Adjusted R-squared:  0.2454 
## F-statistic: 93.19 on 2 and 565 DF,  p-value: < 2.2e-16

This suggests that once controlling for income, Spanish language ability is no longer a strong predictor of leaving home during the shelter-in-place order.

Correlations with increase in staying home

Now I consider looking at correlations with the change in percent of devices staying completely at home since shelter-in-place started relative to the pre-shelter-in-place levels. I plot the change in percentage staying completely at home, and show linear fitting models for the change in percent staying at home, as well as the fractional increase in percent staying home.

# collect the demographic variables
sj_dem_distancing <- sj_internet_by_block %>% 
  dplyr::select(`percent high speed`, `% not completely at home`, `% Completely at Home`, blockgroup) %>% 
  left_join(sj_education_by_block %>% dplyr::select(blockgroup, `percent associates or higher`)) %>% 
  left_join(sj_ami_by_block %>% dplyr::select(blockgroup, `% over 125,000`)) %>% 
  left_join(sj_ami_by_block %>% dplyr::select(blockgroup, `% over 100,000`)) %>% 
  left_join(sj_ami_by_block %>% dplyr::select(blockgroup, `% over 75,000`)) %>% 
  left_join(sj_age_by_block %>% dplyr::select(blockgroup, `percent less than 30`)) %>% 
  left_join(sj_age_by_block %>% dplyr::select(blockgroup, `percent elderly`)) %>% 
  left_join(sj_lang_by_block %>% dplyr::select(blockgroup, `% not speaking spanish`)) %>% 
  left_join(sj_lang_by_block %>% dplyr::select(blockgroup, `% speaking english > well`)) %>% 
  left_join(sj_no_vehicles_by_block %>% dplyr::select(blockgroup, `percent with vehicles`)) %>%
  left_join(sj_occupants_per_room_by_block %>% dplyr::select(blockgroup, `percent less than 1`)) %>% 
  left_join(sj_sex_workers_by_block %>% dplyr::select(blockgroup, `% male workers`)) %>%
  left_join(sj_race_by_block %>% dplyr::select(blockgroup, `% white`, `% Asian`, `% non hispanic/latino`)) %>%
  left_join(sj_age_by_block %>% dplyr::select(blockgroup, `percent less than 18`)) %>%
  left_join(sj_age_by_block %>% dplyr::select(blockgroup, `percent 20-29`))

sj_dem_distancing_pre_post <- sj_dem_distancing %>% 
  left_join(sj_internet_by_block %>% dplyr::select(`% not completely at home pre shelter`, `% Completely at Home Pre Shelter`, blockgroup)) %>% 
  mutate(`% increase in staying completely home` = `% Completely at Home` - `% Completely at Home Pre Shelter`, frac_increase = `% increase in staying completely home`/`% Completely at Home Pre Shelter`) %>%
  mutate(`% hispanic/latino` = (100 - `% non hispanic/latino`))

sj_dem_distancing[is.na(sj_dem_distancing)] <- 0
sj_dem_distancing_pre_post[is.na(sj_dem_distancing_pre_post)] <- 0

saveRDS(sj_dem_distancing_pre_post, "/Users/simonespeizer/Documents/2020 Spring Quarter/CEE 218Z/covid19/Simone_Speizer/sj_socialdistancing_demdata_prepostdifs_manyvars.rds")

# sj_dem_distancing_pre_post <- readRDS("/Users/simonespeizer/Documents/2020 Spring Quarter/CEE 218Z/covid19/Simone_Speizer/sj_socialdistancing_demdata_prepostdifs_manyvars.rds")

Age

# age
sj_dem_distancing_pre_post %>%
  ggplot(aes(
  x = `percent less than 30`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
labs(
    x = "Percent of residents younger than 30",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Young Age Groups"
  )

young_model_dif <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`percent less than 30`)
summary(young_model_dif)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`percent less than 30`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.371  -5.352  -0.198   5.401  30.989 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                       36.97144    1.67774  22.036
## sj_dem_distancing_pre_post$`percent less than 30` -0.28614    0.04253  -6.728
##                                                   Pr(>|t|)    
## (Intercept)                                        < 2e-16 ***
## sj_dem_distancing_pre_post$`percent less than 30` 4.21e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.199 on 567 degrees of freedom
## Multiple R-squared:  0.07394,    Adjusted R-squared:  0.0723 
## F-statistic: 45.27 on 1 and 567 DF,  p-value: 4.209e-11
young_model_frac <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`percent less than 30`)
summary(young_model_frac)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`percent less than 30`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0404 -0.3861 -0.1013  0.2908  2.8689 
## 
## Coefficients:
##                                                    Estimate Std. Error t value
## (Intercept)                                        2.080149   0.116428  17.866
## sj_dem_distancing_pre_post$`percent less than 30` -0.021266   0.002951  -7.205
##                                                   Pr(>|t|)    
## (Intercept)                                        < 2e-16 ***
## sj_dem_distancing_pre_post$`percent less than 30` 1.85e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6384 on 567 degrees of freedom
## Multiple R-squared:  0.08389,    Adjusted R-squared:  0.08227 
## F-statistic: 51.92 on 1 and 567 DF,  p-value: 1.852e-12
sj_dem_distancing_pre_post %>% filter(`percent elderly` < 50) %>% # get rid of extreme outliers
  ggplot(aes(
  x = `percent elderly`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents 65 and older",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Elderly Population"
  )

elderly_model_dif <- lm(`% increase in staying completely home` ~ `percent elderly`, sj_dem_distancing_pre_post %>% filter(`percent elderly` < 50))
summary(elderly_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `percent elderly`, 
##     data = sj_dem_distancing_pre_post %>% filter(`percent elderly` < 
##         50))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.162  -5.596  -0.409   5.698  31.761 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       22.08337    0.87415  25.263  < 2e-16 ***
## `percent elderly`  0.30467    0.06036   5.048 6.05e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.334 on 564 degrees of freedom
## Multiple R-squared:  0.04322,    Adjusted R-squared:  0.04153 
## F-statistic: 25.48 on 1 and 564 DF,  p-value: 6.046e-07
elderly_model_frac <- lm(frac_increase ~ `percent elderly`, sj_dem_distancing_pre_post %>% filter(`percent elderly` < 50))
summary(elderly_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `percent elderly`, data = sj_dem_distancing_pre_post %>% 
##     filter(`percent elderly` < 50))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.58615 -0.41623 -0.09599  0.30691  2.91590 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.946092   0.060638  15.602  < 2e-16 ***
## `percent elderly` 0.024725   0.004187   5.905 6.09e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6475 on 564 degrees of freedom
## Multiple R-squared:  0.05823,    Adjusted R-squared:  0.05656 
## F-statistic: 34.87 on 1 and 564 DF,  p-value: 6.087e-09
sj_dem_distancing_pre_post %>%
  ggplot(aes(
  x = `percent less than 18`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
labs(
    x = "Percent of residents younger than 18",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Child Population"
  )

child_model_dif <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`percent less than 18`)
summary(child_model_dif)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`percent less than 18`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.813  -5.942  -0.159   5.855  30.740 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                       21.86853    1.37778  15.872
## sj_dem_distancing_pre_post$`percent less than 18`  0.18322    0.05871   3.121
##                                                   Pr(>|t|)    
## (Intercept)                                         <2e-16 ***
## sj_dem_distancing_pre_post$`percent less than 18`   0.0019 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.478 on 567 degrees of freedom
## Multiple R-squared:  0.01689,    Adjusted R-squared:  0.01515 
## F-statistic: 9.739 on 1 and 567 DF,  p-value: 0.001895
child_model_frac <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`percent less than 18`)
summary(child_model_frac)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`percent less than 18`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6495 -0.4660 -0.1078  0.3188  2.7406 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                       0.963196   0.096052  10.028
## sj_dem_distancing_pre_post$`percent less than 18` 0.013373   0.004093   3.267
##                                                   Pr(>|t|)    
## (Intercept)                                        < 2e-16 ***
## sj_dem_distancing_pre_post$`percent less than 18`  0.00115 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6608 on 567 degrees of freedom
## Multiple R-squared:  0.01848,    Adjusted R-squared:  0.01675 
## F-statistic: 10.68 on 1 and 567 DF,  p-value: 0.001151
sj_dem_distancing_pre_post %>%
  ggplot(aes(
  x = `percent 20-29`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
labs(
    x = "Percent of residents ages 20-29",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Young Adult Population"
  )

young_adult_model_dif <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`percent 20-29`)
summary(young_adult_model_dif)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`percent 20-29`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -31.3972  -5.2744  -0.0801   5.2712  31.1251 
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                                31.99720    0.73528  43.517   <2e-16
## sj_dem_distancing_pre_post$`percent 20-29` -0.43367    0.04574  -9.481   <2e-16
##                                               
## (Intercept)                                ***
## sj_dem_distancing_pre_post$`percent 20-29` ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.881 on 567 degrees of freedom
## Multiple R-squared:  0.1368, Adjusted R-squared:  0.1353 
## F-statistic: 89.89 on 1 and 567 DF,  p-value: < 2.2e-16
young_adult_model_frac <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`percent 20-29`)
summary(young_adult_model_frac)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`percent 20-29`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.67805 -0.39960 -0.08361  0.29977  2.61537 
## 
## Coefficients:
##                                             Estimate Std. Error t value
## (Intercept)                                 1.717783   0.050598   33.95
## sj_dem_distancing_pre_post$`percent 20-29` -0.032757   0.003147  -10.41
##                                            Pr(>|t|)    
## (Intercept)                                  <2e-16 ***
## sj_dem_distancing_pre_post$`percent 20-29`   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6112 on 567 degrees of freedom
## Multiple R-squared:  0.1604, Adjusted R-squared:  0.1589 
## F-statistic: 108.3 on 1 and 567 DF,  p-value: < 2.2e-16

Looking at all age categories present

Here I look at each age bracket individually and see the effect size.

sj_all_age_by_block <- sj_all_age_by_block %>% 
  left_join(sj_dem_distancing_pre_post %>% dplyr::select(blockgroup, `% increase in staying completely home`)) %>%
  mutate(`% 80 and older` = (`80 to 84 years` + `85 years and over`)*100/total)

for (i in 2:(ncol(sj_all_age_by_block)-3)) {
  colName <- colnames(sj_all_age_by_block)[i]
  columnToUse <- sj_all_age_by_block %>% dplyr::select(blockgroup, colName, total)
  percent_vals <- (columnToUse[,2]*100)/columnToUse$total
  print(colName)
  age_bracket_model <- lm(sj_all_age_by_block$`% increase in staying completely home` ~ percent_vals[,1])
  print(summary(age_bracket_model))
}
## [1] "10 to 14 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.989  -5.772  -0.251   6.106  31.107 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        22.6945     0.8456  26.839  < 2e-16 ***
## percent_vals[, 1]   0.5097     0.1159   4.399  1.3e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.4 on 567 degrees of freedom
## Multiple R-squared:  0.033,  Adjusted R-squared:  0.0313 
## F-statistic: 19.35 on 1 and 567 DF,  p-value: 1.299e-05
## 
## [1] "15 to 17 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.590  -5.703  -0.299   6.025  28.762 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        23.9548     0.7319  32.729  < 2e-16 ***
## percent_vals[, 1]   0.5440     0.1647   3.303  0.00102 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.469 on 567 degrees of freedom
## Multiple R-squared:  0.01887,    Adjusted R-squared:  0.01714 
## F-statistic: 10.91 on 1 and 567 DF,  p-value: 0.001018
## 
## [1] "18 and 19 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.983  -5.726  -0.313   5.555  32.183 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        26.9832     0.5096  52.947  < 2e-16 ***
## percent_vals[, 1]  -0.4841     0.1549  -3.126  0.00186 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.478 on 567 degrees of freedom
## Multiple R-squared:  0.01694,    Adjusted R-squared:  0.01521 
## F-statistic: 9.772 on 1 and 567 DF,  p-value: 0.001863
## 
## [1] "20 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.037  -5.937  -0.337   5.844  30.463 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        27.0367     0.4878   55.42  < 2e-16 ***
## percent_vals[, 1]  -0.8129     0.2203   -3.69 0.000246 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.447 on 567 degrees of freedom
## Multiple R-squared:  0.02346,    Adjusted R-squared:  0.02173 
## F-statistic: 13.62 on 1 and 567 DF,  p-value: 0.0002455
## 
## [1] "21 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.799  -6.011  -0.260   5.586  31.872 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        27.7094     0.4820   57.49  < 2e-16 ***
## percent_vals[, 1]  -1.3987     0.2316   -6.04 2.78e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.266 on 567 degrees of freedom
## Multiple R-squared:  0.06046,    Adjusted R-squared:  0.0588 
## F-statistic: 36.49 on 1 and 567 DF,  p-value: 2.784e-09
## 
## [1] "22 to 24 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.392  -5.787  -0.292   5.508  30.849 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        29.2916     0.6200  47.242  < 2e-16 ***
## percent_vals[, 1]  -0.8348     0.1227  -6.806 2.56e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.191 on 567 degrees of freedom
## Multiple R-squared:  0.07552,    Adjusted R-squared:  0.07389 
## F-statistic: 46.32 on 1 and 567 DF,  p-value: 2.562e-11
## 
## [1] "25 to 29 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.380  -5.172  -0.203   5.482  34.778 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       29.54566    0.65504  45.105  < 2e-16 ***
## percent_vals[, 1] -0.48262    0.07178  -6.724 4.33e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.2 on 567 degrees of freedom
## Multiple R-squared:  0.07385,    Adjusted R-squared:  0.07221 
## F-statistic: 45.21 on 1 and 567 DF,  p-value: 4.327e-11
## 
## [1] "30 to 34 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.169  -5.719   0.054   6.108  28.876 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       28.62409    0.76260  37.535  < 2e-16 ***
## percent_vals[, 1] -0.35981    0.08895  -4.045 5.96e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.424 on 567 degrees of freedom
## Multiple R-squared:  0.02805,    Adjusted R-squared:  0.02633 
## F-statistic: 16.36 on 1 and 567 DF,  p-value: 5.956e-05
## 
## [1] "35 to 39 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.178  -5.931  -0.342   5.886  30.945 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        27.1001     0.9283   29.19   <2e-16 ***
## percent_vals[, 1]  -0.1541     0.1158   -1.33    0.184    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.545 on 567 degrees of freedom
## Multiple R-squared:  0.003112,   Adjusted R-squared:  0.001354 
## F-statistic:  1.77 on 1 and 567 DF,  p-value: 0.1839
## 
## [1] "40 to 44 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.904  -5.864  -0.229   5.725  29.702 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        24.0449     0.9790  24.560   <2e-16 ***
## percent_vals[, 1]   0.2690     0.1239   2.171   0.0304 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.52 on 567 degrees of freedom
## Multiple R-squared:  0.008243,   Adjusted R-squared:  0.006494 
## F-statistic: 4.713 on 1 and 567 DF,  p-value: 0.03036
## 
## [1] "45 to 49 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.833  -5.939  -0.224   5.619  29.100 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        22.4085     0.9112  24.593  < 2e-16 ***
## percent_vals[, 1]   0.4955     0.1138   4.354 1.58e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.404 on 567 degrees of freedom
## Multiple R-squared:  0.03236,    Adjusted R-squared:  0.03065 
## F-statistic: 18.96 on 1 and 567 DF,  p-value: 1.584e-05
## 
## [1] "5 to 9 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.107  -5.944  -0.152   5.796  30.971 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        24.1977     0.8763  27.615   <2e-16 ***
## percent_vals[, 1]   0.2914     0.1272   2.292   0.0223 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.515 on 567 degrees of freedom
## Multiple R-squared:  0.009177,   Adjusted R-squared:  0.00743 
## F-statistic: 5.252 on 1 and 567 DF,  p-value: 0.02229
## 
## [1] "50 to 54 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -45.426  -5.480  -0.427   5.792  27.931 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        22.3476     0.8544  26.157  < 2e-16 ***
## percent_vals[, 1]   0.5074     0.1058   4.795 2.08e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.371 on 567 degrees of freedom
## Multiple R-squared:  0.03897,    Adjusted R-squared:  0.03728 
## F-statistic: 22.99 on 1 and 567 DF,  p-value: 2.079e-06
## 
## [1] "55 to 59 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.321  -5.867  -0.114   5.616  31.636 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        24.4962     0.8143  30.083   <2e-16 ***
## percent_vals[, 1]   0.2297     0.1095   2.099   0.0363 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.523 on 567 degrees of freedom
## Multiple R-squared:  0.007707,   Adjusted R-squared:  0.005957 
## F-statistic: 4.404 on 1 and 567 DF,  p-value: 0.0363
## 
## [1] "60 and 61 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.186  -6.006  -0.248   5.744  31.983 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        25.1857     0.6689  37.654   <2e-16 ***
## percent_vals[, 1]   0.3240     0.2172   1.492    0.136    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.541 on 567 degrees of freedom
## Multiple R-squared:  0.003911,   Adjusted R-squared:  0.002154 
## F-statistic: 2.226 on 1 and 567 DF,  p-value: 0.1363
## 
## [1] "62 to 64 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.708  -6.026  -0.223   5.733  32.871 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        24.2843     0.7159  33.924  < 2e-16 ***
## percent_vals[, 1]   0.5286     0.1849   2.859  0.00441 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.491 on 567 degrees of freedom
## Multiple R-squared:  0.01421,    Adjusted R-squared:  0.01247 
## F-statistic: 8.173 on 1 and 567 DF,  p-value: 0.004407
## 
## [1] "65 and 66 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.955  -5.925  -0.190   5.921  32.001 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        25.4986     0.6466  39.434   <2e-16 ***
## percent_vals[, 1]   0.2541     0.2649   0.959    0.338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.552 on 567 degrees of freedom
## Multiple R-squared:  0.00162,    Adjusted R-squared:  -0.0001409 
## F-statistic:  0.92 on 1 and 567 DF,  p-value: 0.3379
## 
## [1] "67 to 69 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.865  -5.674  -0.273   5.697  32.543 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        24.5857     0.6293  39.066  < 2e-16 ***
## percent_vals[, 1]   0.5700     0.1985   2.871  0.00425 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.491 on 567 degrees of freedom
## Multiple R-squared:  0.01433,    Adjusted R-squared:  0.01259 
## F-statistic: 8.242 on 1 and 567 DF,  p-value: 0.004246
## 
## [1] "70 to 74 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.884  -5.820  -0.349   5.807  31.850 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        25.2424     0.6412  39.367   <2e-16 ***
## percent_vals[, 1]   0.2305     0.1554   1.483    0.139    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.541 on 567 degrees of freedom
## Multiple R-squared:  0.003863,   Adjusted R-squared:  0.002106 
## F-statistic: 2.199 on 1 and 567 DF,  p-value: 0.1387
## 
## [1] "75 to 79 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.139  -5.968  -0.135   5.853  31.264 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        25.1390     0.5502  45.694   <2e-16 ***
## percent_vals[, 1]   0.3468     0.1552   2.235   0.0258 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.518 on 567 degrees of freedom
## Multiple R-squared:  0.008733,   Adjusted R-squared:  0.006984 
## F-statistic: 4.995 on 1 and 567 DF,  p-value: 0.02581
## 
## [1] "80 to 84 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.332  -5.475   0.005   5.789  28.268 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        24.3324     0.5325  45.698  < 2e-16 ***
## percent_vals[, 1]   1.0399     0.2257   4.608 5.02e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.385 on 567 degrees of freedom
## Multiple R-squared:  0.0361, Adjusted R-squared:  0.0344 
## F-statistic: 21.24 on 1 and 567 DF,  p-value: 5.022e-06
## 
## [1] "85 years and over"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.873  -5.887  -0.173   5.869  31.569 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       25.87296    0.51524  50.216   <2e-16 ***
## percent_vals[, 1]  0.06881    0.19787   0.348    0.728    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.558 on 567 degrees of freedom
## Multiple R-squared:  0.0002132,  Adjusted R-squared:  -0.00155 
## F-statistic: 0.1209 on 1 and 567 DF,  p-value: 0.7282
## 
## [1] "Under 5 years"
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     percent_vals[, 1])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.346  -5.684   0.088   5.939  29.968 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        27.9474     0.8369  33.393  < 2e-16 ***
## percent_vals[, 1]  -0.3192     0.1198  -2.665  0.00791 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.5 on 567 degrees of freedom
## Multiple R-squared:  0.01237,    Adjusted R-squared:  0.01063 
## F-statistic: 7.103 on 1 and 567 DF,  p-value: 0.007914
summary(lm(sj_all_age_by_block$`% increase in staying completely home` ~ sj_all_age_by_block$`% 80 and older`))
## 
## Call:
## lm(formula = sj_all_age_by_block$`% increase in staying completely home` ~ 
##     sj_all_age_by_block$`% 80 and older`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.892  -5.836  -0.180   5.700  30.432 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           24.8917     0.5666  43.930  < 2e-16 ***
## sj_all_age_by_block$`% 80 and older`   0.3390     0.1249   2.714  0.00686 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.498 on 567 degrees of freedom
## Multiple R-squared:  0.01282,    Adjusted R-squared:  0.01108 
## F-statistic: 7.364 on 1 and 567 DF,  p-value: 0.006858

Income

# income - less than $75000
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% over 75,000`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $75,000 (50% AMI) annually",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Households Above 50% AMI"
  )

income_75_model_dif <- lm(`% increase in staying completely home` ~ `% over 75,000`, sj_dem_distancing_pre_post)
summary(income_75_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% over 75,000`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.248  -4.180   0.456   4.620  24.878 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      8.10256    1.18011   6.866 1.74e-11 ***
## `% over 75,000`  0.28765    0.01821  15.799  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.966 on 567 degrees of freedom
## Multiple R-squared:  0.3057, Adjusted R-squared:  0.3044 
## F-statistic: 249.6 on 1 and 567 DF,  p-value: < 2.2e-16
income_75_model_frac <- lm(frac_increase ~ `% over 75,000`, sj_dem_distancing_pre_post)
summary(income_75_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% over 75,000`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.72742 -0.30605 -0.04073  0.29059  2.51466 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.064774   0.083717   0.774    0.439    
## `% over 75,000` 0.019284   0.001292  14.931   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5651 on 567 degrees of freedom
## Multiple R-squared:  0.2822, Adjusted R-squared:  0.281 
## F-statistic: 222.9 on 1 and 567 DF,  p-value: < 2.2e-16
# income - less than $100000
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% over 100,000`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $100,000 (80% AMI) annually",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Households Below 80% AMI"
  )

income_100_model_dif <- lm(`% increase in staying completely home` ~ `% over 100,000`, sj_dem_distancing_pre_post)
summary(income_100_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% over 100,000`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.603  -4.175   0.734   5.018  21.595 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      11.25846    0.90960   12.38   <2e-16 ***
## `% over 100,000`  0.28914    0.01669   17.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.73 on 567 degrees of freedom
## Multiple R-squared:  0.3462, Adjusted R-squared:  0.3451 
## F-statistic: 300.2 on 1 and 567 DF,  p-value: < 2.2e-16
income_100_model_frac <- lm(frac_increase ~ `% over 100,000`, sj_dem_distancing_pre_post)
summary(income_100_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% over 100,000`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82682 -0.31981 -0.01839  0.26521  2.68925 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.254910   0.064069   3.979 7.83e-05 ***
## `% over 100,000` 0.019805   0.001175  16.851  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5444 on 567 degrees of freedom
## Multiple R-squared:  0.3337, Adjusted R-squared:  0.3325 
## F-statistic: 283.9 on 1 and 567 DF,  p-value: < 2.2e-16
# income - less than $125000
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% over 125,000`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) +
  labs(
    x = "Percent of housholds with incomes over $125,000 annually",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Households Below $125,000"
  )

income_125_model_dif <- lm(`% increase in staying completely home` ~ `% over 125,000`, sj_dem_distancing_pre_post)
summary(income_125_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% over 125,000`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.465  -3.935   0.927   4.907  21.255 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      13.38710    0.75357   17.77   <2e-16 ***
## `% over 125,000`  0.30678    0.01665   18.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.56 on 567 degrees of freedom
## Multiple R-squared:  0.3746, Adjusted R-squared:  0.3735 
## F-statistic: 339.6 on 1 and 567 DF,  p-value: < 2.2e-16
income_125_model_frac <- lm(frac_increase ~ `% over 125,000`, sj_dem_distancing_pre_post)
summary(income_125_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% over 125,000`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.90642 -0.29574 -0.00501  0.25612  2.57094 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.374004   0.052192   7.166 2.42e-12 ***
## `% over 125,000` 0.021664   0.001153  18.789  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5236 on 567 degrees of freedom
## Multiple R-squared:  0.3837, Adjusted R-squared:  0.3826 
## F-statistic:   353 on 1 and 567 DF,  p-value: < 2.2e-16

Language

# language
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% speaking english > well`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of individuals speaking English well",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and English Language Ability"
  )

english_ability_model_dif <- lm(`% increase in staying completely home` ~ `% speaking english > well`, sj_dem_distancing_pre_post)
summary(english_ability_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% speaking english > well`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.594  -4.552   0.305   5.103  30.299 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -5.96604    3.63137  -1.643    0.101    
## `% speaking english > well`  0.36018    0.04072   8.846   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.961 on 567 degrees of freedom
## Multiple R-squared:  0.1213, Adjusted R-squared:  0.1197 
## F-statistic: 78.26 on 1 and 567 DF,  p-value: < 2.2e-16
english_ability_model_frac <- lm(frac_increase ~ `% speaking english > well`, sj_dem_distancing_pre_post)
summary(english_ability_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% speaking english > well`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.55871 -0.35286 -0.03063  0.28273  2.70000 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 -1.366510   0.246418  -5.546  4.5e-08 ***
## `% speaking english > well`  0.029650   0.002763  10.731  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6081 on 567 degrees of freedom
## Multiple R-squared:  0.1688, Adjusted R-squared:  0.1674 
## F-statistic: 115.2 on 1 and 567 DF,  p-value: < 2.2e-16
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% not speaking spanish`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of individuals not speaking Spanish",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Spanish Language Ability"
  )

spanish_speaking_model_dif <- lm(`% increase in staying completely home` ~ `% not speaking spanish`, sj_dem_distancing_pre_post)
summary(spanish_speaking_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% not speaking spanish`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.456  -3.586   0.750   5.080  26.180 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.34954    1.39163   5.281 1.83e-07 ***
## `% not speaking spanish`  0.23971    0.01734  13.827  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.267 on 567 degrees of freedom
## Multiple R-squared:  0.2522, Adjusted R-squared:  0.2508 
## F-statistic: 191.2 on 1 and 567 DF,  p-value: < 2.2e-16
spanish_speaking_model_frac <- lm(frac_increase ~ `% not speaking spanish`, sj_dem_distancing_pre_post)
summary(spanish_speaking_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% not speaking spanish`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.62776 -0.31980 -0.02877  0.27021  2.47133 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              -0.043619   0.096919   -0.45    0.653    
## `% not speaking spanish`  0.016815   0.001207   13.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5757 on 567 degrees of freedom
## Multiple R-squared:  0.2549, Adjusted R-squared:  0.2536 
## F-statistic:   194 on 1 and 567 DF,  p-value: < 2.2e-16

Occupants per room

# occupants per room
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `percent less than 1`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of households with 1 or fewer occupant per room",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Room Occupancy"
  )

occupants_model_dif <- lm(`% increase in staying completely home` ~ `percent less than 1`, sj_dem_distancing_pre_post)
summary(occupants_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `percent less than 1`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.715  -4.498   0.298   5.055  27.842 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -7.22883    2.97184  -2.432   0.0153 *  
## `percent less than 1`  0.36886    0.03276  11.260   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.642 on 567 degrees of freedom
## Multiple R-squared:  0.1828, Adjusted R-squared:  0.1813 
## F-statistic: 126.8 on 1 and 567 DF,  p-value: < 2.2e-16
occupants_model_frac <- lm(frac_increase ~ `percent less than 1`, sj_dem_distancing_pre_post)
summary(occupants_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `percent less than 1`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5701 -0.3835 -0.0605  0.2602  2.5787 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -1.149711   0.205380  -5.598 3.38e-08 ***
## `percent less than 1`  0.026802   0.002264  11.839  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5972 on 567 degrees of freedom
## Multiple R-squared:  0.1982, Adjusted R-squared:  0.1968 
## F-statistic: 140.2 on 1 and 567 DF,  p-value: < 2.2e-16

Vehicle ownership

# vehicles - percent with no vehicles
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `percent with vehicles`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of housholds with vehicles available",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Vehicle Availability"
  )

vehicles_model_dif <- lm(`% increase in staying completely home` ~ `percent with vehicles`, sj_dem_distancing_pre_post)
summary(vehicles_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `percent with vehicles`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.787  -5.768  -0.271   5.232  29.732 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -7.88779    4.87608  -1.618    0.106    
## `percent with vehicles`  0.35656    0.05117   6.969 8.92e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.175 on 567 degrees of freedom
## Multiple R-squared:  0.07889,    Adjusted R-squared:  0.07726 
## F-statistic: 48.56 on 1 and 567 DF,  p-value: 8.921e-12
vehicles_model_frac <- lm(frac_increase ~ `percent with vehicles`, sj_dem_distancing_pre_post)
summary(vehicles_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `percent with vehicles`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7224 -0.4244 -0.1149  0.3004  2.8641 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -0.906858   0.342485  -2.648  0.00833 ** 
## `percent with vehicles`  0.022848   0.003594   6.357 4.22e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6444 on 567 degrees of freedom
## Multiple R-squared:  0.06654,    Adjusted R-squared:  0.06489 
## F-statistic: 40.42 on 1 and 567 DF,  p-value: 4.222e-10

Education

sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `percent associates or higher`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of people with an degree at Associate's level or higher",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Education"
  )

educ_model_dif <- lm(`% increase in staying completely home` ~ `percent associates or higher`, sj_dem_distancing_pre_post)
summary(educ_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `percent associates or higher`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.024  -3.195   0.959   4.990  22.843 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    12.92391    0.87475   14.77   <2e-16 ***
## `percent associates or higher`  0.27700    0.01716   16.14   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.913 on 567 degrees of freedom
## Multiple R-squared:  0.3147, Adjusted R-squared:  0.3135 
## F-statistic: 260.4 on 1 and 567 DF,  p-value: < 2.2e-16
educ_model_frac <- lm(frac_increase ~ `percent associates or higher`, sj_dem_distancing_pre_post)
summary(educ_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `percent associates or higher`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.24512 -0.29235 -0.00326  0.28250  2.55065 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    0.352492   0.061037   5.775 1.27e-08 ***
## `percent associates or higher` 0.019324   0.001198  16.134  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5522 on 567 degrees of freedom
## Multiple R-squared:  0.3146, Adjusted R-squared:  0.3134 
## F-statistic: 260.3 on 1 and 567 DF,  p-value: < 2.2e-16

Internet

sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `percent high speed`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of households with broadband such as cable, fiber optic or DSL",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and High Speed Internet"
  )

internet_model_dif <- lm(`% increase in staying completely home` ~ `percent high speed`, sj_dem_distancing_pre_post)
summary(internet_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `percent high speed`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.275  -4.808   0.533   5.350  27.409 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -3.19418    2.33393  -1.369    0.172    
## `percent high speed`  0.36232    0.02864  12.649   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.442 on 567 degrees of freedom
## Multiple R-squared:  0.2201, Adjusted R-squared:  0.2187 
## F-statistic:   160 on 1 and 567 DF,  p-value: < 2.2e-16
internet_model_frac <- lm(frac_increase ~ `percent high speed`, sj_dem_distancing_pre_post)
summary(internet_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `percent high speed`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.68581 -0.33803 -0.07178  0.25763  2.56825 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -0.647258   0.165554   -3.91 0.000104 ***
## `percent high speed`  0.023728   0.002032   11.68  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5988 on 567 degrees of freedom
## Multiple R-squared:  0.1939, Adjusted R-squared:  0.1925 
## F-statistic: 136.4 on 1 and 567 DF,  p-value: < 2.2e-16

Sex of workers

sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% male workers`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of workers that are male",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Sex of Workers"
  )

sex_workers_model_dif <- lm(`% increase in staying completely home` ~ `% male workers`, sj_dem_distancing_pre_post)
summary(sex_workers_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% male workers`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.145  -5.935  -0.211   5.905  31.467 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      23.95036    6.21488   3.854  0.00013 ***
## `% male workers`  0.03804    0.11591   0.328  0.74291    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.559 on 567 degrees of freedom
## Multiple R-squared:  0.0001899,  Adjusted R-squared:  -0.001573 
## F-statistic: 0.1077 on 1 and 567 DF,  p-value: 0.7429
sex_workers_model_frac <- lm(frac_increase ~ `% male workers`, sj_dem_distancing_pre_post)
summary(sex_workers_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% male workers`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7784 -0.4487 -0.1048  0.3191  2.7785 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      0.711352   0.433040   1.643    0.101
## `% male workers` 0.010323   0.008076   1.278    0.202
## 
## Residual standard error: 0.666 on 567 degrees of freedom
## Multiple R-squared:  0.002873,   Adjusted R-squared:  0.001114 
## F-statistic: 1.634 on 1 and 567 DF,  p-value: 0.2017

Race

# white
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% white`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents that are white",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and White Residents"
  )

white_model_dif <- lm(`% increase in staying completely home` ~ `% white`, sj_dem_distancing_pre_post)
summary(white_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% white`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.933  -5.793  -0.369   5.563  32.323 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 24.70379    0.88859  27.801   <2e-16 ***
## `% white`    0.02977    0.01843   1.615    0.107    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.538 on 567 degrees of freedom
## Multiple R-squared:  0.004581,   Adjusted R-squared:  0.002825 
## F-statistic: 2.609 on 1 and 567 DF,  p-value: 0.1068
white_model_frac <- lm(frac_increase ~ `% white`, sj_dem_distancing_pre_post)
summary(white_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% white`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.72422 -0.41988 -0.09149  0.30784  2.86971 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.998632   0.060878  16.404  < 2e-16 ***
## `% white`   0.006156   0.001263   4.875 1.41e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6534 on 567 degrees of freedom
## Multiple R-squared:  0.04024,    Adjusted R-squared:  0.03854 
## F-statistic: 23.77 on 1 and 567 DF,  p-value: 1.412e-06
# asian
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% Asian`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents that are Asian",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Asian Residents"
  )

asian_model_dif <- lm(`% increase in staying completely home` ~ `% Asian`, sj_dem_distancing_pre_post)
summary(asian_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% Asian`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.878  -5.446  -0.278   5.730  25.557 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  21.4363     0.6861  31.244  < 2e-16 ***
## `% Asian`     0.1402     0.0176   7.964 9.14e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.066 on 567 degrees of freedom
## Multiple R-squared:  0.1006, Adjusted R-squared:  0.09903 
## F-statistic: 63.43 on 1 and 567 DF,  p-value: 9.139e-15
asian_model_frac <- lm(frac_increase ~ `% Asian`, sj_dem_distancing_pre_post)
summary(asian_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% Asian`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7310 -0.4560 -0.1234  0.3023  2.8051 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.092511   0.049733  21.968  < 2e-16 ***
## `% Asian`   0.005274   0.001276   4.134  4.1e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6571 on 567 degrees of freedom
## Multiple R-squared:  0.02926,    Adjusted R-squared:  0.02755 
## F-statistic: 17.09 on 1 and 567 DF,  p-value: 4.099e-05
# hispanic/latino
sj_dem_distancing_pre_post %>% 
  ggplot(aes(
  x = `% non hispanic/latino`,
  y = `% increase in staying completely home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of residents that are not Hispanic or Latino",
    y = "Dif in % completely at home after shelter-in-place relative to before",
    title = "San Jose: Social Distancing and Hispanic/Latino Residents"
  )

hisp_model_dif <- lm(`% increase in staying completely home` ~ `% non hispanic/latino`, sj_dem_distancing_pre_post)
summary(hisp_model_dif)
## 
## Call:
## lm(formula = `% increase in staying completely home` ~ `% non hispanic/latino`, 
##     data = sj_dem_distancing_pre_post)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.292  -3.765   0.875   5.012  24.425 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              10.4901     1.0752   9.757   <2e-16 ***
## `% non hispanic/latino`   0.2278     0.0150  15.181   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.061 on 567 degrees of freedom
## Multiple R-squared:  0.289,  Adjusted R-squared:  0.2877 
## F-statistic: 230.5 on 1 and 567 DF,  p-value: < 2.2e-16
hisp_model_frac <- lm(frac_increase ~ `% non hispanic/latino`, sj_dem_distancing_pre_post)
summary(hisp_model_frac)
## 
## Call:
## lm(formula = frac_increase ~ `% non hispanic/latino`, data = sj_dem_distancing_pre_post)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.72265 -0.34701 -0.02016  0.28153  2.48350 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.202196   0.075564   2.676  0.00767 ** 
## `% non hispanic/latino` 0.015602   0.001054  14.797  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5665 on 567 degrees of freedom
## Multiple R-squared:  0.2786, Adjusted R-squared:  0.2773 
## F-statistic:   219 on 1 and 567 DF,  p-value: < 2.2e-16

Multiple regression for increases in staying at home

Multiple regression analysis: income, internet, Spanish language ability, and occupants per room.

First with difference in percents.

difs_model <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent less than 1` + sj_dem_distancing_pre_post$`percent high speed`)
summary(difs_model)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish` + 
##         sj_dem_distancing_pre_post$`percent less than 1` + sj_dem_distancing_pre_post$`percent high speed`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.375  -3.581   0.687   4.711  20.908 
## 
## Coefficients:
##                                                      Estimate Std. Error
## (Intercept)                                          5.899964   3.152979
## sj_dem_distancing_pre_post$`% over 125,000`          0.229622   0.023580
## sj_dem_distancing_pre_post$`% not speaking spanish`  0.084737   0.024407
## sj_dem_distancing_pre_post$`percent less than 1`    -0.002893   0.042081
## sj_dem_distancing_pre_post$`percent high speed`      0.053743   0.035259
##                                                     t value Pr(>|t|)    
## (Intercept)                                           1.871 0.061830 .  
## sj_dem_distancing_pre_post$`% over 125,000`           9.738  < 2e-16 ***
## sj_dem_distancing_pre_post$`% not speaking spanish`   3.472 0.000557 ***
## sj_dem_distancing_pre_post$`percent less than 1`     -0.069 0.945214    
## sj_dem_distancing_pre_post$`percent high speed`       1.524 0.128009    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.42 on 564 degrees of freedom
## Multiple R-squared:  0.4007, Adjusted R-squared:  0.3965 
## F-statistic: 94.28 on 4 and 564 DF,  p-value: < 2.2e-16

Second with fractional change.

frac_model <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent less than 1` + sj_dem_distancing_pre_post$`percent high speed`)
summary(frac_model)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent less than 1` + 
##     sj_dem_distancing_pre_post$`percent high speed`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88211 -0.28985  0.01105  0.25025  2.47573 
## 
## Coefficients:
##                                                       Estimate Std. Error
## (Intercept)                                         -9.937e-02  2.188e-01
## sj_dem_distancing_pre_post$`% over 125,000`          1.714e-02  1.636e-03
## sj_dem_distancing_pre_post$`% not speaking spanish`  5.666e-03  1.693e-03
## sj_dem_distancing_pre_post$`percent less than 1`     2.438e-03  2.920e-03
## sj_dem_distancing_pre_post$`percent high speed`     -8.851e-06  2.446e-03
##                                                     t value Pr(>|t|)    
## (Intercept)                                          -0.454 0.649839    
## sj_dem_distancing_pre_post$`% over 125,000`          10.474  < 2e-16 ***
## sj_dem_distancing_pre_post$`% not speaking spanish`   3.346 0.000876 ***
## sj_dem_distancing_pre_post$`percent less than 1`      0.835 0.403996    
## sj_dem_distancing_pre_post$`percent high speed`      -0.004 0.997115    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5148 on 564 degrees of freedom
## Multiple R-squared:  0.4074, Adjusted R-squared:  0.4032 
## F-statistic: 96.92 on 4 and 564 DF,  p-value: < 2.2e-16

Multiple regression analysis: income and Spanish language ability

difs_model_inc_span <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish`)
summary(difs_model_inc_span)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.357  -3.676   0.924   4.571  21.519 
## 
## Coefficients:
##                                                     Estimate Std. Error t value
## (Intercept)                                          8.61183    1.25411   6.867
## sj_dem_distancing_pre_post$`% over 125,000`          0.24519    0.02092  11.720
## sj_dem_distancing_pre_post$`% not speaking spanish`  0.09395    0.01992   4.716
##                                                     Pr(>|t|)    
## (Intercept)                                         1.73e-11 ***
## sj_dem_distancing_pre_post$`% over 125,000`          < 2e-16 ***
## sj_dem_distancing_pre_post$`% not speaking spanish` 3.04e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.422 on 566 degrees of freedom
## Multiple R-squared:  0.3982, Adjusted R-squared:  0.3961 
## F-statistic: 187.3 on 2 and 566 DF,  p-value: < 2.2e-16
frac_model_inc_span <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish`)
summary(frac_model_inc_span)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% not speaking spanish`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.89438 -0.28513  0.01266  0.25143  2.48667 
## 
## Coefficients:
##                                                     Estimate Std. Error t value
## (Intercept)                                         0.046145   0.086888   0.531
## sj_dem_distancing_pre_post$`% over 125,000`         0.017436   0.001449  12.029
## sj_dem_distancing_pre_post$`% not speaking spanish` 0.006451   0.001380   4.673
##                                                     Pr(>|t|)    
## (Intercept)                                            0.596    
## sj_dem_distancing_pre_post$`% over 125,000`          < 2e-16 ***
## sj_dem_distancing_pre_post$`% not speaking spanish` 3.71e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5142 on 566 degrees of freedom
## Multiple R-squared:  0.4066, Adjusted R-squared:  0.4045 
## F-statistic: 193.9 on 2 and 566 DF,  p-value: < 2.2e-16

When only accounting for for income, Spanish language ability is only slightly relevant, though the result is still nontrivial. Let’s try accounting for both education and income level.

Multiple regression analysis: income, education, and Spanish language ability

difs_model_inc_span_educ <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish` +  sj_dem_distancing_pre_post$`percent associates or higher`)
summary(difs_model_inc_span_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish` + 
##         sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.624  -3.586   0.945   4.759  20.957 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                9.65331    1.27940
## sj_dem_distancing_pre_post$`% over 125,000`                0.21037    0.02310
## sj_dem_distancing_pre_post$`% not speaking spanish`        0.03879    0.02551
## sj_dem_distancing_pre_post$`percent associates or higher`  0.09919    0.02906
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 7.545 1.82e-13 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 9.106  < 2e-16 ***
## sj_dem_distancing_pre_post$`% not speaking spanish`         1.520 0.128958    
## sj_dem_distancing_pre_post$`percent associates or higher`   3.413 0.000689 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.353 on 565 degrees of freedom
## Multiple R-squared:  0.4104, Adjusted R-squared:  0.4072 
## F-statistic: 131.1 on 3 and 565 DF,  p-value: < 2.2e-16
frac_model_inc_span_educ <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% not speaking spanish` +  sj_dem_distancing_pre_post$`percent associates or higher`)
summary(frac_model_inc_span_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.84121 -0.28341  0.00487  0.24457  2.51719 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               0.113747   0.088752
## sj_dem_distancing_pre_post$`% over 125,000`               0.015175   0.001603
## sj_dem_distancing_pre_post$`% not speaking spanish`       0.002870   0.001770
## sj_dem_distancing_pre_post$`percent associates or higher` 0.006439   0.002016
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 1.282  0.20050    
## sj_dem_distancing_pre_post$`% over 125,000`                 9.469  < 2e-16 ***
## sj_dem_distancing_pre_post$`% not speaking spanish`         1.622  0.10543    
## sj_dem_distancing_pre_post$`percent associates or higher`   3.193  0.00148 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5101 on 565 degrees of freedom
## Multiple R-squared:  0.4171, Adjusted R-squared:  0.414 
## F-statistic: 134.8 on 3 and 565 DF,  p-value: < 2.2e-16

The effect of Spanish language speaking vanishes when accounting for both education and income.

Multiple regression analysis: income, English language ability and education

difs_model_inc_eng_educ <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% speaking english > well` +  sj_dem_distancing_pre_post$`percent associates or higher`)
summary(difs_model_inc_eng_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% speaking english > well` + 
##         sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.661  -3.333   0.880   4.649  20.973 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               25.18077    3.52954
## sj_dem_distancing_pre_post$`% over 125,000`                0.23349    0.02296
## sj_dem_distancing_pre_post$`% speaking english > well`    -0.19524    0.04768
## sj_dem_distancing_pre_post$`percent associates or higher`  0.18101    0.02580
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 7.134 3.00e-12 ***
## sj_dem_distancing_pre_post$`% over 125,000`                10.171  < 2e-16 ***
## sj_dem_distancing_pre_post$`% speaking english > well`     -4.095 4.84e-05 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   7.015 6.61e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.262 on 565 degrees of freedom
## Multiple R-squared:  0.425,  Adjusted R-squared:  0.422 
## F-statistic: 139.2 on 3 and 565 DF,  p-value: < 2.2e-16
frac_model_inc_eng_educ <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% speaking english > well` +  sj_dem_distancing_pre_post$`percent associates or higher`)
summary(frac_model_inc_eng_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% speaking english > well` + 
##     sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8319 -0.2744  0.0116  0.2402  2.5344 
## 
## Coefficients:
##                                                            Estimate Std. Error
## (Intercept)                                                0.538662   0.248145
## sj_dem_distancing_pre_post$`% over 125,000`                0.015973   0.001614
## sj_dem_distancing_pre_post$`% speaking english > well`    -0.004388   0.003352
## sj_dem_distancing_pre_post$`percent associates or higher`  0.009720   0.001814
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 2.171   0.0304 *  
## sj_dem_distancing_pre_post$`% over 125,000`                 9.897  < 2e-16 ***
## sj_dem_distancing_pre_post$`% speaking english > well`     -1.309   0.1911    
## sj_dem_distancing_pre_post$`percent associates or higher`   5.358 1.23e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5105 on 565 degrees of freedom
## Multiple R-squared:  0.4162, Adjusted R-squared:  0.4131 
## F-statistic: 134.3 on 3 and 565 DF,  p-value: < 2.2e-16

English language ability is actually a slightly better predictor than Spanish language ability, when also accounting for education and income.

Multiple regression analysis: income, English language ability, education, Spanish language ability, and vehicle ownership

difs_model_lots <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% speaking english > well` +  sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent with vehicles`)
summary(difs_model_lots)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% speaking english > well` + 
##         sj_dem_distancing_pre_post$`percent associates or higher` + 
##         sj_dem_distancing_pre_post$`% not speaking spanish` + 
##         sj_dem_distancing_pre_post$`percent with vehicles`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.133  -3.433   1.112   4.632  21.106 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               17.65811    5.22625
## sj_dem_distancing_pre_post$`% over 125,000`                0.21340    0.02466
## sj_dem_distancing_pre_post$`% speaking english > well`    -0.20722    0.04775
## sj_dem_distancing_pre_post$`percent associates or higher`  0.15189    0.03097
## sj_dem_distancing_pre_post$`% not speaking spanish`        0.04909    0.02521
## sj_dem_distancing_pre_post$`percent with vehicles`         0.07334    0.04480
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 3.379 0.000779 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 8.652  < 2e-16 ***
## sj_dem_distancing_pre_post$`% speaking english > well`     -4.340 1.69e-05 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   4.905 1.23e-06 ***
## sj_dem_distancing_pre_post$`% not speaking spanish`         1.947 0.051974 .  
## sj_dem_distancing_pre_post$`percent with vehicles`          1.637 0.102172    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.235 on 563 degrees of freedom
## Multiple R-squared:  0.4312, Adjusted R-squared:  0.4262 
## F-statistic: 85.37 on 5 and 563 DF,  p-value: < 2.2e-16
frac_model_lots <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% speaking english > well` +  sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent with vehicles`)
summary(frac_model_lots)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% speaking english > well` + 
##     sj_dem_distancing_pre_post$`percent associates or higher` + 
##     sj_dem_distancing_pre_post$`% not speaking spanish` + sj_dem_distancing_pre_post$`percent with vehicles`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.84150 -0.29296  0.00345  0.25028  2.50474 
## 
## Coefficients:
##                                                            Estimate Std. Error
## (Intercept)                                                0.314763   0.368372
## sj_dem_distancing_pre_post$`% over 125,000`                0.015270   0.001738
## sj_dem_distancing_pre_post$`% speaking english > well`    -0.004946   0.003366
## sj_dem_distancing_pre_post$`percent associates or higher`  0.007694   0.002183
## sj_dem_distancing_pre_post$`% not speaking spanish`        0.003113   0.001777
## sj_dem_distancing_pre_post$`percent with vehicles`         0.001639   0.003158
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 0.854 0.393208    
## sj_dem_distancing_pre_post$`% over 125,000`                 8.784  < 2e-16 ***
## sj_dem_distancing_pre_post$`% speaking english > well`     -1.469 0.142270    
## sj_dem_distancing_pre_post$`percent associates or higher`   3.525 0.000458 ***
## sj_dem_distancing_pre_post$`% not speaking spanish`         1.752 0.080339 .  
## sj_dem_distancing_pre_post$`percent with vehicles`          0.519 0.603837    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.51 on 563 degrees of freedom
## Multiple R-squared:  0.4195, Adjusted R-squared:  0.4144 
## F-statistic: 81.38 on 5 and 563 DF,  p-value: < 2.2e-16

The main important variables are education and income, with potentially some effect of English language ability.

Multiple regression analysis: income and education

difs_model_inc_educ <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`percent associates or higher`)
summary(difs_model_inc_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.536  -3.474   0.935   4.783  20.732 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               11.12744    0.83575
## sj_dem_distancing_pre_post$`% over 125,000`                0.21576    0.02286
## sj_dem_distancing_pre_post$`percent associates or higher`  0.12719    0.02251
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 13.31  < 2e-16 ***
## sj_dem_distancing_pre_post$`% over 125,000`                  9.44  < 2e-16 ***
## sj_dem_distancing_pre_post$`percent associates or higher`    5.65 2.55e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.362 on 566 degrees of freedom
## Multiple R-squared:  0.408,  Adjusted R-squared:  0.4059 
## F-statistic:   195 on 2 and 566 DF,  p-value: < 2.2e-16
frac_model_inc_educ <- lm(sj_dem_distancing_pre_post$`frac_increase` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`percent associates or higher`)
summary(frac_model_inc_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82730 -0.27606  0.00481  0.25344  2.54945 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               0.222815   0.057992
## sj_dem_distancing_pre_post$`% over 125,000`               0.015574   0.001586
## sj_dem_distancing_pre_post$`percent associates or higher` 0.008510   0.001562
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 3.842 0.000136 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 9.820  < 2e-16 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   5.448 7.63e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5108 on 566 degrees of freedom
## Multiple R-squared:  0.4144, Adjusted R-squared:  0.4123 
## F-statistic: 200.3 on 2 and 566 DF,  p-value: < 2.2e-16

Comparing this to earlier models, we see that adding the English language ability variable does add some predictive power, though not much, and adding the vehicle ownership and Spanish language ability variables have negligible effects.

We now consider adding race into the regressions.

Multiple regression analysis: Hispanic/Latino and income

difs_model_inc_hisp <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% non hispanic/latino`)
summary(difs_model_inc_hisp)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% non hispanic/latino`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.000  -3.597   0.769   4.686  20.530 
## 
## Coefficients:
##                                                    Estimate Std. Error t value
## (Intercept)                                         9.53147    0.98397   9.687
## sj_dem_distancing_pre_post$`% over 125,000`         0.22753    0.02108  10.796
## sj_dem_distancing_pre_post$`% non hispanic/latino`  0.10450    0.01781   5.867
##                                                    Pr(>|t|)    
## (Intercept)                                         < 2e-16 ***
## sj_dem_distancing_pre_post$`% over 125,000`         < 2e-16 ***
## sj_dem_distancing_pre_post$`% non hispanic/latino` 7.57e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.347 on 566 degrees of freedom
## Multiple R-squared:  0.4104, Adjusted R-squared:  0.4083 
## F-statistic:   197 on 2 and 566 DF,  p-value: < 2.2e-16
frac_model_inc_hisp <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% non hispanic/latino`)
summary(frac_model_inc_hisp)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% non hispanic/latino`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.89541 -0.29602  0.01761  0.24967  2.51911 
## 
## Coefficients:
##                                                    Estimate Std. Error t value
## (Intercept)                                        0.131886   0.068516   1.925
## sj_dem_distancing_pre_post$`% over 125,000`        0.016688   0.001468  11.371
## sj_dem_distancing_pre_post$`% non hispanic/latino` 0.006562   0.001240   5.291
##                                                    Pr(>|t|)    
## (Intercept)                                          0.0547 .  
## sj_dem_distancing_pre_post$`% over 125,000`         < 2e-16 ***
## sj_dem_distancing_pre_post$`% non hispanic/latino` 1.75e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5116 on 566 degrees of freedom
## Multiple R-squared:  0.4128, Adjusted R-squared:  0.4107 
## F-statistic: 198.9 on 2 and 566 DF,  p-value: < 2.2e-16

Multiple regression analysis: Hispanic/Latino, income, and education

difs_model_inc_hisp_educ <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% non hispanic/latino` + sj_dem_distancing_pre_post$`percent associates or higher`)
summary(difs_model_inc_hisp_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% non hispanic/latino` + 
##         sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.991  -3.502   0.893   4.772  20.505 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                9.68175    0.98252
## sj_dem_distancing_pre_post$`% over 125,000`                0.20615    0.02299
## sj_dem_distancing_pre_post$`% non hispanic/latino`         0.06681    0.02423
## sj_dem_distancing_pre_post$`percent associates or higher`  0.06981    0.03056
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 9.854  < 2e-16 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 8.968  < 2e-16 ***
## sj_dem_distancing_pre_post$`% non hispanic/latino`          2.757  0.00602 ** 
## sj_dem_distancing_pre_post$`percent associates or higher`   2.284  0.02272 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.319 on 565 degrees of freedom
## Multiple R-squared:  0.4158, Adjusted R-squared:  0.4127 
## F-statistic: 134.1 on 3 and 565 DF,  p-value: < 2.2e-16
frac_model_inc_hisp_educ <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% non hispanic/latino` + sj_dem_distancing_pre_post$`percent associates or higher`)
summary(frac_model_inc_hisp_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% non hispanic/latino` + sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.85044 -0.28600  0.01185  0.23880  2.52843 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               0.143421   0.068348
## sj_dem_distancing_pre_post$`% over 125,000`               0.015047   0.001599
## sj_dem_distancing_pre_post$`% non hispanic/latino`        0.003669   0.001686
## sj_dem_distancing_pre_post$`percent associates or higher` 0.005359   0.002126
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 2.098   0.0363 *  
## sj_dem_distancing_pre_post$`% over 125,000`                 9.409   <2e-16 ***
## sj_dem_distancing_pre_post$`% non hispanic/latino`          2.177   0.0299 *  
## sj_dem_distancing_pre_post$`percent associates or higher`   2.521   0.0120 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5092 on 565 degrees of freedom
## Multiple R-squared:  0.4193, Adjusted R-squared:  0.4162 
## F-statistic:   136 on 3 and 565 DF,  p-value: < 2.2e-16

When including education, percentage of Hispanic/Latino residents loses as much of its predictive power.

Multiple regression analysis: income, education, and white residents

difs_model_inc_white_educ <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% white` + sj_dem_distancing_pre_post$`percent associates or higher`)
summary(difs_model_inc_white_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% white` + 
##         sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.332  -3.526   1.077   4.585  17.568 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               12.81844    0.90049
## sj_dem_distancing_pre_post$`% over 125,000`                0.22410    0.02254
## sj_dem_distancing_pre_post$`% white`                      -0.06793    0.01483
## sj_dem_distancing_pre_post$`percent associates or higher`  0.14608    0.02251
##                                                           t value Pr(>|t|)    
## (Intercept)                                                14.235  < 2e-16 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 9.944  < 2e-16 ***
## sj_dem_distancing_pre_post$`% white`                       -4.581 5.69e-06 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   6.491 1.87e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.235 on 565 degrees of freedom
## Multiple R-squared:  0.4292, Adjusted R-squared:  0.4261 
## F-statistic: 141.6 on 3 and 565 DF,  p-value: < 2.2e-16
frac_model_inc_white_educ <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% white` + sj_dem_distancing_pre_post$`percent associates or higher`)
summary(frac_model_inc_white_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% white` + sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82110 -0.27150  0.00171  0.25161  2.55499 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                0.2272842  0.0636330
## sj_dem_distancing_pre_post$`% over 125,000`                0.0155963  0.0015925
## sj_dem_distancing_pre_post$`% white`                      -0.0001795  0.0010477
## sj_dem_distancing_pre_post$`percent associates or higher`  0.0085599  0.0015904
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 3.572 0.000385 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 9.793  < 2e-16 ***
## sj_dem_distancing_pre_post$`% white`                       -0.171 0.864006    
## sj_dem_distancing_pre_post$`percent associates or higher`   5.382 1.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5113 on 565 degrees of freedom
## Multiple R-squared:  0.4144, Adjusted R-squared:  0.4113 
## F-statistic: 133.3 on 3 and 565 DF,  p-value: < 2.2e-16

Multiple regression analysis: income, education, and Asian residents

difs_model_inc_asian_educ <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher`)
summary(difs_model_inc_asian_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% Asian` + 
##         sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.705  -3.705   0.857   4.634  17.829 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                9.60424    0.85925
## sj_dem_distancing_pre_post$`% over 125,000`                0.21227    0.02228
## sj_dem_distancing_pre_post$`% Asian`                       0.08002    0.01438
## sj_dem_distancing_pre_post$`percent associates or higher`  0.10745    0.02222
##                                                           t value Pr(>|t|)    
## (Intercept)                                                11.177  < 2e-16 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 9.526  < 2e-16 ***
## sj_dem_distancing_pre_post$`% Asian`                        5.564 4.08e-08 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   4.835 1.72e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.175 on 565 degrees of freedom
## Multiple R-squared:  0.4387, Adjusted R-squared:  0.4357 
## F-statistic: 147.2 on 3 and 565 DF,  p-value: < 2.2e-16
frac_model_inc_asian_educ <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher`)
summary(frac_model_inc_asian_educ)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8069 -0.2789  0.0056  0.2408  2.5652 
## 
## Coefficients:
##                                                            Estimate Std. Error
## (Intercept)                                               0.2081541  0.0612043
## sj_dem_distancing_pre_post$`% over 125,000`               0.0155406  0.0015872
## sj_dem_distancing_pre_post$`% Asian`                      0.0007702  0.0010245
## sj_dem_distancing_pre_post$`percent associates or higher` 0.0083200  0.0015831
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 3.401 0.000719 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 9.791  < 2e-16 ***
## sj_dem_distancing_pre_post$`% Asian`                        0.752 0.452473    
## sj_dem_distancing_pre_post$`percent associates or higher`   5.256 2.09e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.511 on 565 degrees of freedom
## Multiple R-squared:  0.415,  Adjusted R-squared:  0.4119 
## F-statistic: 133.6 on 3 and 565 DF,  p-value: < 2.2e-16

Multiple regression analysis: income, education, Asian residents, and English language ability

difs_model_inc_asian_educ_eng <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`% speaking english > well`)
summary(difs_model_inc_asian_educ_eng)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% Asian` + 
##         sj_dem_distancing_pre_post$`percent associates or higher` + 
##         sj_dem_distancing_pre_post$`% speaking english > well`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.293  -3.755   0.918   4.684  17.925 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               13.84272    4.57478
## sj_dem_distancing_pre_post$`% over 125,000`                0.21782    0.02305
## sj_dem_distancing_pre_post$`% Asian`                       0.06956    0.01816
## sj_dem_distancing_pre_post$`percent associates or higher`  0.12550    0.02933
## sj_dem_distancing_pre_post$`% speaking english > well`    -0.05612    0.05949
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 3.026 0.002592 ** 
## sj_dem_distancing_pre_post$`% over 125,000`                 9.450  < 2e-16 ***
## sj_dem_distancing_pre_post$`% Asian`                        3.830 0.000143 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   4.279  2.2e-05 ***
## sj_dem_distancing_pre_post$`% speaking english > well`     -0.943 0.345943    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.175 on 564 degrees of freedom
## Multiple R-squared:  0.4396, Adjusted R-squared:  0.4356 
## F-statistic: 110.6 on 4 and 564 DF,  p-value: < 2.2e-16
frac_model_inc_asian_educ_eng <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`% speaking english > well`)
summary(frac_model_inc_asian_educ_eng)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + 
##     sj_dem_distancing_pre_post$`% speaking english > well`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.83412 -0.27408  0.01231  0.24078  2.53228 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                0.5510989  0.3257852
## sj_dem_distancing_pre_post$`% over 125,000`                0.0159899  0.0016414
## sj_dem_distancing_pre_post$`% Asian`                      -0.0000763  0.0012935
## sj_dem_distancing_pre_post$`percent associates or higher`  0.0097805  0.0020886
## sj_dem_distancing_pre_post$`% speaking english > well`    -0.0045405  0.0042366
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 1.692   0.0913 .  
## sj_dem_distancing_pre_post$`% over 125,000`                 9.742  < 2e-16 ***
## sj_dem_distancing_pre_post$`% Asian`                       -0.059   0.9530    
## sj_dem_distancing_pre_post$`percent associates or higher`   4.683 3.55e-06 ***
## sj_dem_distancing_pre_post$`% speaking english > well`     -1.072   0.2843    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.511 on 564 degrees of freedom
## Multiple R-squared:  0.4162, Adjusted R-squared:  0.4121 
## F-statistic: 100.5 on 4 and 564 DF,  p-value: < 2.2e-16

Multiple regression analysis: income, education, Asian residents, and residents ages 20-29

difs_model_inc_asian_educ_youngadult <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`percent 20-29`)
summary(difs_model_inc_asian_educ_youngadult)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% Asian` + 
##         sj_dem_distancing_pre_post$`percent associates or higher` + 
##         sj_dem_distancing_pre_post$`percent 20-29`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.952  -3.518   1.046   4.572  17.144 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                               13.82849    1.13204
## sj_dem_distancing_pre_post$`% over 125,000`                0.17397    0.02279
## sj_dem_distancing_pre_post$`% Asian`                       0.08269    0.01403
## sj_dem_distancing_pre_post$`percent associates or higher`  0.11267    0.02168
## sj_dem_distancing_pre_post$`percent 20-29`                -0.21529    0.03882
##                                                           t value Pr(>|t|)    
## (Intercept)                                                12.216  < 2e-16 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 7.634 9.78e-14 ***
## sj_dem_distancing_pre_post$`% Asian`                        5.895 6.45e-09 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   5.197 2.84e-07 ***
## sj_dem_distancing_pre_post$`percent 20-29`                 -5.546 4.50e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.993 on 564 degrees of freedom
## Multiple R-squared:  0.4677, Adjusted R-squared:  0.464 
## F-statistic: 123.9 on 4 and 564 DF,  p-value: < 2.2e-16
frac_model_inc_asian_educ_youngadult <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`percent 20-29`)
summary(frac_model_inc_asian_educ_youngadult)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + 
##     sj_dem_distancing_pre_post$`percent 20-29`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.94950 -0.27087 -0.00095  0.24284  2.42641 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                0.5451457  0.0800736
## sj_dem_distancing_pre_post$`% over 125,000`                0.0124857  0.0016120
## sj_dem_distancing_pre_post$`% Asian`                       0.0009829  0.0009921
## sj_dem_distancing_pre_post$`percent associates or higher`  0.0087366  0.0015337
## sj_dem_distancing_pre_post$`percent 20-29`                -0.0171748  0.0027458
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 6.808 2.54e-11 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 7.746 4.44e-14 ***
## sj_dem_distancing_pre_post$`% Asian`                        0.991    0.322    
## sj_dem_distancing_pre_post$`percent associates or higher`   5.697 1.97e-08 ***
## sj_dem_distancing_pre_post$`percent 20-29`                 -6.255 7.86e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4946 on 564 degrees of freedom
## Multiple R-squared:  0.453,  Adjusted R-squared:  0.4491 
## F-statistic: 116.7 on 4 and 564 DF,  p-value: < 2.2e-16

Though looking at percent less than 30 doesn’t have predictive power with these variables, percent of young adults does.

Multiple regression analysis: income, education, Asian residents, and residents less than 18

difs_model_inc_asian_educ_child <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`percent less than 18`)
summary(difs_model_inc_asian_educ_child)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% Asian` + 
##         sj_dem_distancing_pre_post$`percent associates or higher` + 
##         sj_dem_distancing_pre_post$`percent less than 18`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.178  -3.662   0.894   4.515  16.915 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                2.47455    1.39831
## sj_dem_distancing_pre_post$`% over 125,000`                0.18418    0.02200
## sj_dem_distancing_pre_post$`% Asian`                       0.08819    0.01397
## sj_dem_distancing_pre_post$`percent associates or higher`  0.14233    0.02218
## sj_dem_distancing_pre_post$`percent less than 18`          0.28365    0.04474
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 1.770   0.0773 .  
## sj_dem_distancing_pre_post$`% over 125,000`                 8.372 4.49e-16 ***
## sj_dem_distancing_pre_post$`% Asian`                        6.314 5.53e-10 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   6.416 2.97e-10 ***
## sj_dem_distancing_pre_post$`percent less than 18`           6.339 4.73e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.938 on 564 degrees of freedom
## Multiple R-squared:  0.476,  Adjusted R-squared:  0.4723 
## F-statistic: 128.1 on 4 and 564 DF,  p-value: < 2.2e-16
frac_model_inc_asian_educ_child <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`percent less than 18`)
summary(frac_model_inc_asian_educ_child)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + 
##     sj_dem_distancing_pre_post$`percent less than 18`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.89245 -0.26883  0.00138  0.24275  2.69787 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                               -0.2637545  0.1000841
## sj_dem_distancing_pre_post$`% over 125,000`                0.0136814  0.0015746
## sj_dem_distancing_pre_post$`% Asian`                       0.0013107  0.0009998
## sj_dem_distancing_pre_post$`percent associates or higher`  0.0106285  0.0015879
## sj_dem_distancing_pre_post$`percent less than 18`          0.0187744  0.0032025
##                                                           t value Pr(>|t|)    
## (Intercept)                                                -2.635  0.00864 ** 
## sj_dem_distancing_pre_post$`% over 125,000`                 8.689  < 2e-16 ***
## sj_dem_distancing_pre_post$`% Asian`                        1.311  0.19040    
## sj_dem_distancing_pre_post$`percent associates or higher`   6.693 5.27e-11 ***
## sj_dem_distancing_pre_post$`percent less than 18`           5.862 7.77e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4966 on 564 degrees of freedom
## Multiple R-squared:  0.4486, Adjusted R-squared:  0.4447 
## F-statistic: 114.7 on 4 and 564 DF,  p-value: < 2.2e-16

Similarly, looking at percent of children is relevant as well.

Multiple regression analysis: income, education, Asian residents, and residents less than 18 and ages 20-29

difs_model_inc_asian_educ_child_yad <- lm(sj_dem_distancing_pre_post$`% increase in staying completely home` ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`percent less than 18` + sj_dem_distancing_pre_post$`percent 20-29`)
summary(difs_model_inc_asian_educ_child_yad)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$`% increase in staying completely home` ~ 
##     sj_dem_distancing_pre_post$`% over 125,000` + sj_dem_distancing_pre_post$`% Asian` + 
##         sj_dem_distancing_pre_post$`percent associates or higher` + 
##         sj_dem_distancing_pre_post$`percent less than 18` + sj_dem_distancing_pre_post$`percent 20-29`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.311  -3.331   0.836   4.324  16.506 
## 
## Coefficients:
##                                                           Estimate Std. Error
## (Intercept)                                                6.85199    1.91126
## sj_dem_distancing_pre_post$`% over 125,000`                0.16596    0.02248
## sj_dem_distancing_pre_post$`% Asian`                       0.08802    0.01385
## sj_dem_distancing_pre_post$`percent associates or higher`  0.13763    0.02204
## sj_dem_distancing_pre_post$`percent less than 18`          0.21799    0.04854
## sj_dem_distancing_pre_post$`percent 20-29`                -0.13899    0.04179
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 3.585 0.000366 ***
## sj_dem_distancing_pre_post$`% over 125,000`                 7.382 5.65e-13 ***
## sj_dem_distancing_pre_post$`% Asian`                        6.357 4.24e-10 ***
## sj_dem_distancing_pre_post$`percent associates or higher`   6.246 8.31e-10 ***
## sj_dem_distancing_pre_post$`percent less than 18`           4.491 8.62e-06 ***
## sj_dem_distancing_pre_post$`percent 20-29`                 -3.326 0.000938 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.877 on 563 degrees of freedom
## Multiple R-squared:  0.4861, Adjusted R-squared:  0.4816 
## F-statistic: 106.5 on 5 and 563 DF,  p-value: < 2.2e-16
frac_model_inc_asian_educ_child_yad <- lm(sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` +  sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + sj_dem_distancing_pre_post$`percent less than 18` + sj_dem_distancing_pre_post$`percent 20-29`)
summary(frac_model_inc_asian_educ_child_yad)
## 
## Call:
## lm(formula = sj_dem_distancing_pre_post$frac_increase ~ sj_dem_distancing_pre_post$`% over 125,000` + 
##     sj_dem_distancing_pre_post$`% Asian` + sj_dem_distancing_pre_post$`percent associates or higher` + 
##     sj_dem_distancing_pre_post$`percent less than 18` + sj_dem_distancing_pre_post$`percent 20-29`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.97058 -0.27467  0.00494  0.25271  2.55282 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                0.1363538  0.1359484
## sj_dem_distancing_pre_post$`% over 125,000`                0.0120160  0.0015992
## sj_dem_distancing_pre_post$`% Asian`                       0.0012952  0.0009848
## sj_dem_distancing_pre_post$`percent associates or higher`  0.0101988  0.0015674
## sj_dem_distancing_pre_post$`percent less than 18`          0.0127733  0.0034529
## sj_dem_distancing_pre_post$`percent 20-29`                -0.0127039  0.0029722
##                                                           t value Pr(>|t|)    
## (Intercept)                                                 1.003 0.316301    
## sj_dem_distancing_pre_post$`% over 125,000`                 7.514 2.27e-13 ***
## sj_dem_distancing_pre_post$`% Asian`                        1.315 0.188973    
## sj_dem_distancing_pre_post$`percent associates or higher`   6.507 1.70e-10 ***
## sj_dem_distancing_pre_post$`percent less than 18`           3.699 0.000237 ***
## sj_dem_distancing_pre_post$`percent 20-29`                 -4.274 2.25e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4892 on 563 degrees of freedom
## Multiple R-squared:  0.4659, Adjusted R-squared:  0.4612 
## F-statistic: 98.24 on 5 and 563 DF,  p-value: < 2.2e-16

Conclusion from multiple regression analyses on change in leaving home behavior

From the results presented above, we see that income (making over $125,000) predicts about 37% of the variability in percent of devices leaving the home across blockgroups. Adding in education leads to a prediction of about 40% of the variation, and including percent of residents that are Asian with both education and income adds about 2% predictive power. Adding both percent of residents that are children as well as percent of residents ages 20-29 raises the regression to predicting about 47% of the variation in the data.

Testing animating the plot

# another collection for pre shelter in place behavior
sj_dem_distancing_pre_shelter <- sj_dem_distancing %>% 
  dplyr::select(-`% not completely at home`) %>%
  left_join(sj_internet_by_block %>% dplyr::select(`% not completely at home pre shelter`, blockgroup))

# relabel column for leaving home
colnames(sj_dem_distancing_pre_shelter)[ncol(sj_dem_distancing_pre_shelter)] <- "% not completely at home"

sj_dem_distancing[is.na(sj_dem_distancing)] <- 0
sj_dem_distancing_pre_shelter[is.na(sj_dem_distancing_pre_shelter)] <- 0

# add column indicating before or after shelter in place, then bind the two sets of data
sj_dem_distancing_pre_shelter <- sj_dem_distancing_pre_shelter %>% 
  mutate(
    income_trendline =
      fitted(lm(sj_dem_distancing_pre_shelter$`% not completely at home` ~ sj_dem_distancing_pre_shelter$`% over 125,000`))
  ) %>% 
  cbind(`Before or After Shelter-in-Place` = "before")
sj_dem_distancing <-
  sj_dem_distancing %>%
  mutate(
    income_trendline =
      fitted(lm(sj_dem_distancing$`% not completely at home` ~ sj_dem_distancing$`% over 125,000`))
  ) %>% 
  cbind(`Before or After Shelter-in-Place` = "after") %>% 
  rbind(sj_dem_distancing_pre_shelter)

# try animating
fig <- 
  plot_ly (sj_dem_distancing) %>%
    add_trace(
      x = ~`% over 125,000`, 
      y = ~`% not completely at home`, 
      frame = ~`Before or After Shelter-in-Place`, 
      type = 'scatter', 
      mode = 'markers'
    ) %>% 
    add_trace(
      x = ~`% over 125,000`,
      y = ~income_trendline,
      type = 'scatter',
      mode = 'lines',
      line = list(size = 5, color = 'rgba(255, 165, 0, 1)'),
      frame = ~`Before or After Shelter-in-Place`
    ) %>% 
  animation_button(visible = F)
fig
# # save as rds
# saveRDS(sj_dem_distancing, "/Users/simonespeizer/pCloud Drive/Shared/SFBI/Restricted Data Library/Safegraph/covid19analysis/sj_sd_dem_data.rds")


# fig <- plot_ly(sj_dem_distancing) %>% 
#   add_trace(
#     x = ~`% over 125,000`,
#     y = ~`% not completely at home`,
#     frame = ~`Before or After Shelter-in-Place`,
#     type = "scatter",
#     mode = "markers",
#     name = "Under $125,000",
#     marker = list(size = 5, color = 'rgba(50, 150, 200, 1)'),
#     visible = T
#   ) %>% 
#   add_trace(
#     x = ~`% over 125,000`,
#     y = fitted(lm(sj_dem_distancing$`% not completely at home` ~ sj_dem_distancing$`% over 125,000`)),
#     name = 'trendline',
#     mode = 'lines',
#     line = list(size = 5, color = 'rgba(255, 165, 0, 1)'),
#     frame = ~`Before or After Shelter-in-Place`,
#     visible = F
#   ) %>%
#   add_trace(
#     x = ~`% not speaking spanish`,
#     y = ~`% not completely at home`,
#     frame = ~`Before or After Shelter-in-Place`,
#     name = "speak spanish",
#     marker = list(size = 5, color = 'rgba(50, 150, 200, 1)'),
#     visible = F
#   ) %>% 
#   add_trace(
#     x = ~`% not speaking spanish`,
#     y = fitted(lm(sj_dem_distancing$`% not completely at home` ~ sj_dem_distancing$`% not speaking spanish`)),
#     name = 'trendline',
#     mode = 'lines',
#     line = list(size = 5, color = 'rgba(255, 165, 0, 1)'),
#     frame = ~`Before or After Shelter-in-Place`,
#     visible = F
#   ) %>% 
#   add_trace(
#     x = ~`percent associates or higher`,
#     y = ~`% not completely at home`,
#     frame = ~`Before or After Shelter-in-Place`,
#     name = "percent higher degree",
#     marker = list(size = 5, color = 'rgba(50, 150, 200, 1)'),
#     visible = F
#   ) %>% 
#   add_trace(
#     x = ~`percent associates or higher`,
#     y = fitted(lm(sj_dem_distancing$`% not completely at home` ~ sj_dem_distancing$`percent associates or higher`)),
#     name = 'trendline',
#     mode = 'lines',
#     line = list(size = 5, color = 'rgba(255, 165, 0, 1)'),
#     frame = ~`Before or After Shelter-in-Place`,
#     visible = F
#   ) %>%
#   add_trace(
#     x = ~`percent high speed`,
#     y = ~`% not completely at home`,
#     frame = ~`Before or After Shelter-in-Place`,
#     name = "percent high speed internet access",
#     marker = list(size = 5, color = 'rgba(50, 150, 200, 1)'),
#     visible = F
#   ) %>% 
#   add_trace(
#     x = ~`percent high speed`,
#     y = fitted(lm(sj_dem_distancing$`% not completely at home` ~ sj_dem_distancing$`percent high speed`)),
#     name = 'trendline',
#     mode = 'lines',
#     line = list(size = 5, color = 'rgba(255, 165, 0, 1)'),
#     frame = ~sj_dem_distancing$`Before or After Shelter-in-Place`,
#     visible = F
#   ) %>%
#   add_trace(
#     x = ~`percent less than 30`,
#     y = ~`% not completely at home`,
#     frame = ~`Before or After Shelter-in-Place`,
#     name = "percent less than 30",
#     marker = list(size = 5, color = 'rgba(50, 150, 200, 1)'),
#     visible = F
#   ) %>% 
#   add_trace(
#     x = ~`percent less than 30`,
#     y = fitted(lm(sj_dem_distancing$`% not completely at home` ~ sj_dem_distancing$`percent less than 30`)),
#     name = 'trendline',
#     mode = 'lines',
#     line = list(size = 5, color = 'rgba(255, 165, 0, 1)'),
#     frame = ~`Before or After Shelter-in-Place`,
#     visible = F
#   ) %>%
#   layout(
#     updatemenus = list(
#       list(
#         active = 0,
#         type = 'buttons',
#         buttons = list(
#           list(
#             label = "Households Under $125,000",
#             method = 'update',
#             args = list(list(visible = c(T, T, F, F, F, F, F, F, F, F)),
#                         list(title = "Under $125,000",
#                              xaxis = list(title = "% Households Under $125,000 in Income")))),
#           list(
#             label = "Speaking Spanish",
#             method = 'update',
#             args = list(list(visible = c(F, F, T, T, F, F, F, F, F, F)),
#                         list(title = "Not Speaking Spanish",
#                              xaxis = list(title = "% Residents Not Speaking Spanish")))),
#           list(
#             label = "Education Level",
#             method = 'update',
#             args= list(list(visible = c(F, F, F, F, T, T, F, F, F, F)),
#                        list(xaxis = list(title = "% Residents With Associate's Degree or Higher")))),
#           list(
#             label = "High Speed Internet",
#             method = 'update',
#             args= list(list(visible = c(F, F, F, F, F, F, T, T, F, F)),
#                        list(xaxis = list(title = "% Households With High Speed Internet Access")))),
#           list(
#             label = "Young Population",
#             method = 'update',
#             args= list(list(visible = c(F, F, F, F, F, F, T, T, F, F)),
#                        list(xaxis = list(title = "% Residents Under Age 30"))))
#           )
#           )
#         ),
#     yaxis = list(title = "% Residents Leaving Home", 
#                  font = list(size = 15)),
#     showlegend = FALSE
#       )
# fig

Experimentation

Experimentation with other variables and other ways of analyzing the social distancing data. First I look at a few other possible variables. I start with units in the structure.

# try getting other variables
# get data on units in structure
sj_units_in_structure_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B25024)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(key = "variable", value = "estimate", -blockgroup) %>% 
  mutate(label = acs_vars$label[match(variable,acs_vars$name)]) %>% 
  dplyr::select(-variable) %>% 
  separate(label, into = c(NA, NA, "units"), sep = "!!") %>% 
  filter(!is.na(units)) %>%
  spread(key = units, value = estimate) %>%
  mutate(total_nums = `1, attached` + `1, detached` + `10 to 19` + `2` + `20 to 49`+ `3 or 4` + `5 to 9`+ `50 or more`+ `Boat, RV, van, etc.`+ `Mobile home`, `percent 20 or more` = (`20 to 49`+`50 or more`)* 100/ total_nums, `percent 1 only` = (`1, attached` + `1, detached`)*100/total_nums, `percent > 1` = 100 - `percent 1 only`) %>%
  left_join(sj_age_by_block %>% dplyr::select_if(!names(.) %in% c("elderly", "percent elderly", "less than 30", "percent less than 30"))) %>%
  filter(!is.na(device_count))

# plot 
sj_units_in_structure_by_block %>% 
  ggplot(aes(
  x = `percent 20 or more`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of structures with more than 20 units",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and 20 or More Units Per Structure"
  )

summary(lm(`% not completely at home` ~ `percent 20 or more`, sj_units_in_structure_by_block))
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent 20 or more`, 
##     data = sj_units_in_structure_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.476  -4.660  -0.255   4.248  35.930 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          51.47635    0.39054 131.810   <2e-16 ***
## `percent 20 or more`  0.01029    0.01957   0.526    0.599    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.07 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0004885,  Adjusted R-squared:  -0.001277 
## F-statistic: 0.2766 on 1 and 566 DF,  p-value: 0.5991
sj_units_in_structure_by_block %>% 
  ggplot(aes(
  x = `percent 1 only`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of structures with only one unit",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Only 1 Unit Per Structure"
  )

summary(lm(`% not completely at home` ~ `percent 1 only`, sj_units_in_structure_by_block))
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent 1 only`, data = sj_units_in_structure_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.404  -4.843  -0.315   4.438  36.073 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      55.28005    0.85487  64.665  < 2e-16 ***
## `percent 1 only` -0.05115    0.01088  -4.699 3.28e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.919 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03755,    Adjusted R-squared:  0.03585 
## F-statistic: 22.08 on 1 and 566 DF,  p-value: 3.283e-06

Household type and size:

# load data on household type and size
sj_house_size_type_by_block <- getCensus(
    name = "acs/acs5",
    vintage = 2018,
    region = "block group:*", 
    regionin = "state:06+county:085",
    vars = "group(B11016)"
  ) %>% 
  mutate(
    blockgroup =
      paste0(state,county,tract,block_group)
  ) %>% 
  select_if(!names(.) %in% c("GEO_ID","state","county","tract","block_group","NAME")) %>% 
  dplyr::select(-c(contains("EA"),contains("MA"),contains("M"))) %>% 
  gather(key = "variable", value = "estimate", -blockgroup) %>% 
  mutate(label = acs_vars$label[match(variable,acs_vars$name)]) %>% 
  dplyr::select(-variable) %>% 
  separate(label, into = c(NA, NA, "type", "size"), sep = "!!") %>% 
  filter(!is.na(type))


# household type
sj_house_type_by_block <- sj_house_size_type_by_block %>% 
  filter(is.na(size)) %>% 
  dplyr::select(-size) %>%
  spread(key = type, value = estimate) %>% 
  mutate(`total households` = `Family households` + `Nonfamily households`, `percent nonfamily` = `Nonfamily households` / `total households`) %>%
  left_join(sj_age_by_block %>% dplyr::select_if(!names(.) %in% c("elderly", "percent elderly", "less than 30", "percent less than 30"))) %>%
  filter(!is.na(device_count))

sj_house_type_by_block %>% 
  ggplot(aes(
  x = `percent nonfamily`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent nonfamily households",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Household Type"
  )

summary(lm(`% not completely at home` ~ `percent nonfamily`, sj_house_type_by_block))
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent nonfamily`, 
##     data = sj_house_type_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.641  -4.755  -0.149   4.389  38.360 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          49.3397     0.6121  80.612  < 2e-16 ***
## `percent nonfamily`   9.2639     2.1244   4.361 1.54e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.939 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0325, Adjusted R-squared:  0.03079 
## F-statistic: 19.02 on 1 and 566 DF,  p-value: 1.541e-05
# household size
sj_house_size_by_block <- sj_house_size_type_by_block %>% 
  filter(!is.na(size)) %>% 
  dplyr::select(-type) %>%
  group_by(blockgroup, size) %>%
  summarize(`total of this size` = sum(estimate)) %>% 
  spread(key = size, value = `total of this size`) %>%
  mutate(total_nums = `1-person household` + `2-person household` + `3-person household` + `4-person household` + `5-person household`+ `6-person household` + `7-or-more person household`, `percent 5 or more` = (`5-person household`+`6-person household` + `7-or-more person household`)* 100/ total_nums, `percent 1 or 2 only` = (`1-person household` + `2-person household`)*100/total_nums) %>%
  left_join(sj_age_by_block %>% dplyr::select_if(!names(.) %in% c("elderly", "percent elderly", "less than 30", "percent less than 30"))) %>%
  filter(!is.na(device_count))

sj_house_size_by_block %>% 
  ggplot(aes(
  x = `percent 5 or more`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of households with 5 or more people",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Households With 5 or More"
  )

summary(lm(`% not completely at home` ~ `percent 5 or more`, sj_house_size_by_block))
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent 5 or more`, 
##     data = sj_house_size_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.937  -4.541  -0.417   4.061  34.347 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         50.00139    0.53948  92.685  < 2e-16 ***
## `percent 5 or more`  0.09024    0.02421   3.727 0.000213 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.974 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.02396,    Adjusted R-squared:  0.02223 
## F-statistic: 13.89 on 1 and 566 DF,  p-value: 0.000213
sj_house_size_by_block %>% 
  ggplot(aes(
  x = `percent 1 or 2 only`,
  y = `% not completely at home`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of households with 1 or 2 people",
    y = "Percent devices leaving home on weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Small Household Size"
  )

summary(lm(`% not completely at home` ~ `percent 1 or 2 only`, sj_house_size_by_block))
## 
## Call:
## lm(formula = `% not completely at home` ~ `percent 1 or 2 only`, 
##     data = sj_house_size_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.861  -4.821  -0.079   4.332  35.732 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           50.61141    0.93470   54.15   <2e-16 ***
## `percent 1 or 2 only`  0.02158    0.01944    1.11    0.267    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.063 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.002173,   Adjusted R-squared:  0.0004097 
## F-statistic: 1.232 on 1 and 566 DF,  p-value: 0.2674

Next I consider different ways of looking at the social distancing data. First I try distance traveled.

# try other ways of looking at the social distancing data
# first look at total distance traveled
sj_sd_distance <- sj_socialdistancing %>% 
  filter(date > shelter_start) %>% 
  group_by(origin_census_block_group) %>% 
  summarize(total_dist_traveled = sum(distance_traveled_from_home), device_count = sum(device_count)) %>%
  mutate(total_dist_per_device = total_dist_traveled / device_count)

sj_distance_testing <- left_join(sj_ami_by_block, sj_sd_distance, by = c("blockgroup" = "origin_census_block_group")) %>% left_join(sj_age_by_block %>% dplyr::select(blockgroup, `percent less than 30`))

sj_distance_testing %>% filter(total_dist_per_device < 500)  %>% 
  ggplot(aes(
  x = `% over 75,000`,
  y = total_dist_per_device
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of housholds with incomes over $75,000 (50% AMI) annually",
    y = "Average distance traveled per device during weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Income, Distance Metric"
  )

This is very skewed by outliers, and probably not a useful metric.

Now I consider including devices that traveled <1km as staying at (or near) home.

sj_sd_range <- sj_socialdistancing %>% 
  filter(weekend == F) %>% 
  filter(date > shelter_start) %>%
  mutate(travel_buckets_split = lapply(bucketed_distance_traveled, function(x) strsplit(x, "<1000")[[1]][2]), less_than_1km = lapply(travel_buckets_split, function(x) strsplit(x, ":")[[1]][2]), less_than_1km = lapply(less_than_1km, function(x) strsplit(x, ",")[[1]][1])) %>%
  mutate(less_than_1km = lapply(less_than_1km, function(x) str_remove(x, "[}]")))  %>% # clean a bit more
  mutate(less_than_1km = as.numeric(less_than_1km), less_than_1km = replace_na(less_than_1km, 0)) %>% 
  mutate(home_or_1km = completely_home_device_count + less_than_1km) %>% 
  group_by(origin_census_block_group) %>% 
  summarize(home_or_1km = sum(home_or_1km), device_count = sum(device_count)) %>% 
  mutate(`% Within 1km of Home` = (home_or_1km/device_count*100) %>% round(1), `% farther than 1km` = (100-`% Within 1km of Home`))

# join this with other data
sj_1km_testing <- left_join(sj_ami_by_block, sj_sd_range, by = c("blockgroup" = "origin_census_block_group")) %>% 
  left_join(sj_occupants_per_room_by_block %>% dplyr::select(`percent less than 1`, blockgroup)) %>%
  left_join(sj_age_by_block %>% dplyr::select(`percent less than 30`, blockgroup)) %>%
  left_join(sj_lang_by_block %>% dplyr::select(`% speaking english > well`, blockgroup)) 

# plot with income
sj_1km_testing %>%  
  ggplot(aes(
  x = `% over 75,000`,
  y = `% farther than 1km`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of housholds with incomes over $75,000 (50% AMI) annually",
    y = "% of devices going farther than 1km of home, weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Income, 1km Range"
  )

summary(lm(`% farther than 1km` ~ `% over 75,000`, sj_1km_testing))
## 
## Call:
## lm(formula = `% farther than 1km` ~ `% over 75,000`, data = sj_1km_testing)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.068  -4.630  -0.633   4.135  32.635 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     64.27172    1.07390   59.85   <2e-16 ***
## `% over 75,000` -0.20381    0.01655  -12.31   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.169 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2113, Adjusted R-squared:  0.2099 
## F-statistic: 151.6 on 1 and 566 DF,  p-value: < 2.2e-16
# plot with age
sj_1km_testing %>%  
  ggplot(aes(
  x = `percent less than 30`,
  y = `% farther than 1km`
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of people younger than 30",
    y = "Percent of devices farther than 1km of home during weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Age, 1km Range"
  )

summary(lm(`% farther than 1km` ~ `percent less than 30`, sj_1km_testing))
## 
## Call:
## lm(formula = `% farther than 1km` ~ `percent less than 30`, data = sj_1km_testing)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.305  -4.595  -0.326   4.013  39.401 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            44.89889    1.46159  30.719  < 2e-16 ***
## `percent less than 30`  0.17542    0.03705   4.735 2.77e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.014 on 567 degrees of freedom
## Multiple R-squared:  0.03803,    Adjusted R-squared:  0.03634 
## F-statistic: 22.42 on 1 and 567 DF,  p-value: 2.775e-06
# run multiple regression model
modeltest2 <- lm(sj_1km_testing$`% farther than 1km` ~ sj_1km_testing$`% over 75,000` + sj_1km_testing$`percent less than 30` + sj_1km_testing$`% speaking english > well` + sj_1km_testing$`percent less than 1`)
summary(modeltest2)
## 
## Call:
## lm(formula = sj_1km_testing$`% farther than 1km` ~ sj_1km_testing$`% over 75,000` + 
##     sj_1km_testing$`percent less than 30` + sj_1km_testing$`% speaking english > well` + 
##     sj_1km_testing$`percent less than 1`)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.815  -4.536  -0.558   4.267  32.459 
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                                59.20631    4.51314  13.119   <2e-16
## sj_1km_testing$`% over 75,000`             -0.21163    0.02083 -10.159   <2e-16
## sj_1km_testing$`percent less than 30`       0.03029    0.04104   0.738   0.4607
## sj_1km_testing$`% speaking english > well`  0.10487    0.04431   2.367   0.0183
## sj_1km_testing$`percent less than 1`       -0.05447    0.04500  -1.210   0.2266
##                                               
## (Intercept)                                ***
## sj_1km_testing$`% over 75,000`             ***
## sj_1km_testing$`percent less than 30`         
## sj_1km_testing$`% speaking english > well` *  
## sj_1km_testing$`percent less than 1`          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.14 on 563 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2217, Adjusted R-squared:  0.2162 
## F-statistic: 40.09 on 4 and 563 DF,  p-value: < 2.2e-16

It looks like the fit of these selected variables is slightly better for the social distancing data based on not traveling farther than 1km.

Now I also consider “non-work” behavior.

sj_nonworking_by_block <- sj_socialdistancing %>% 
  filter(weekend == F) %>% 
  filter(date > shelter_start) %>%
  mutate(nonworking = device_count - completely_home_device_count - part_time_work_behavior_devices - full_time_work_behavior_devices) %>%
  group_by(origin_census_block_group) %>%
  summarize(nonworking_count = sum(nonworking), total_device = sum(device_count)) %>% 
  mutate(nonworking_percent = nonworking_count*100 / total_device, percent_only_work_home = 100-nonworking_percent) %>%
  left_join(sj_1km_testing %>% dplyr::select(`% over 75,000`, `percent less than 30`, `% speaking english > well`, `percent less than 1`, blockgroup), by = c("origin_census_block_group" = "blockgroup"))


# plot against age and income
sj_nonworking_by_block %>%  
  ggplot(aes(
  x = `% over 75,000`,
  y = nonworking_percent
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of housholds with incomes over $75,000 (50% AMI) annually",
    y = "Percent of devices leaving home for non-work purposes during weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Income, Nonworking Behavior"
  )

summary(lm(nonworking_percent ~ `% over 75,000`, sj_nonworking_by_block))
## 
## Call:
## lm(formula = nonworking_percent ~ `% over 75,000`, data = sj_nonworking_by_block)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.5428  -3.8965  -0.6847   3.4523  31.5874 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     52.67676    0.97773   53.88   <2e-16 ***
## `% over 75,000` -0.15797    0.01507  -10.48   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.527 on 566 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1626, Adjusted R-squared:  0.1611 
## F-statistic: 109.9 on 1 and 566 DF,  p-value: < 2.2e-16
sj_nonworking_by_block %>%  
  ggplot(aes(
  x = `percent less than 30`,
  y = nonworking_percent
)) + geom_point() + 
  geom_smooth(method=lm) + 
  labs(
    x = "Percent of people younger than 30",
    y = "Percent of devices leaving home for non-work purposes during weekdays since shelter-in-place",
    title = "San Jose: Social Distancing and Age, Nonworking Behavior"
  )

summary(lm(nonworking_percent ~ `percent less than 30`, sj_nonworking_by_block))
## 
## Call:
## lm(formula = nonworking_percent ~ `percent less than 30`, data = sj_nonworking_by_block)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.960  -4.132  -0.279   3.228  30.570 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            36.27595    1.26879  28.591  < 2e-16 ***
## `percent less than 30`  0.17114    0.03216   5.321 1.49e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.957 on 567 degrees of freedom
## Multiple R-squared:  0.04756,    Adjusted R-squared:  0.04588 
## F-statistic: 28.32 on 1 and 567 DF,  p-value: 1.486e-07
# multiple regression model
modeltest3 <- lm(sj_nonworking_by_block$nonworking_percent ~ sj_nonworking_by_block$`% over 75,000` + sj_nonworking_by_block$`percent less than 30` + sj_nonworking_by_block$`% speaking english > well` + sj_nonworking_by_block$`percent less than 1`)
summary(modeltest3)
## 
## Call:
## lm(formula = sj_nonworking_by_block$nonworking_percent ~ sj_nonworking_by_block$`% over 75,000` + 
##     sj_nonworking_by_block$`percent less than 30` + sj_nonworking_by_block$`% speaking english > well` + 
##     sj_nonworking_by_block$`percent less than 1`)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.3385  -3.8983  -0.7328   3.4219  29.7824 
## 
## Coefficients:
##                                                    Estimate Std. Error t value
## (Intercept)                                        44.59442    4.09629  10.887
## sj_nonworking_by_block$`% over 75,000`             -0.16347    0.01891  -8.646
## sj_nonworking_by_block$`percent less than 30`       0.07344    0.03725   1.972
## sj_nonworking_by_block$`% speaking english > well`  0.08823    0.04022   2.194
## sj_nonworking_by_block$`percent less than 1`       -0.02462    0.04084  -0.603
##                                                    Pr(>|t|)    
## (Intercept)                                          <2e-16 ***
## sj_nonworking_by_block$`% over 75,000`               <2e-16 ***
## sj_nonworking_by_block$`percent less than 30`        0.0491 *  
## sj_nonworking_by_block$`% speaking english > well`   0.0287 *  
## sj_nonworking_by_block$`percent less than 1`         0.5469    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.48 on 563 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1788, Adjusted R-squared:  0.1729 
## F-statistic: 30.64 on 4 and 563 DF,  p-value: < 2.2e-16

These variables do worse for the percent nonworking metric, which makes sense.